Dane Morgan

The diffusion of Mg defects in 3C-SiC is studied using the density functional theory. Mg has the highest burn-in rate as a transmutant in 3C-SiC when it is placed in high-energy neutron irradiation environment of a fusion reactor. The presence and evolution of transmutant defects impact thermal and mechanical properties of this important structural material. This study is focused on understanding the structure, stability, and evolution of Mg defects and the interaction of Mg with native defects in 3C-SiC. Our calculations of diffusion coefficients for different Mg defects suggest that Mg is likely to diffuse faster in pristine 3C-SiC than in the damaged one, in agreement with earlier experimental observations.

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

Perovskite SrVO3 has recently been proposed as a novel electron emission cathode material. Density functional theory (DFT) calculations suggest multiple low work function surfaces, and recent experimental efforts have consistently demonstrated effective work functions of ∼2.7 eV for polycrystalline samples, both results suggesting, but not directly confirming, that some fraction of even lower work function surface is present. In this work, thermionic electron emission microscopy (ThEEM) and high-field ultraviolet photoemission spectroscopy (UPS) are used to study the local work function distribution and measure the work function of a partially oriented- (110)-SrVO3 perovskite oxide cathode surface. Our results show direct evidence of low work function patches of about 2.0 eV on the cathode surface, with a corresponding onset of observable thermionic emission at 750 °C. We hypothesize that, in our ThEEM and UPS experiments, the high applied electric field suppresses the patch field effect, enabling the direct measurement of local work functions. This measured work function of 2.0 eV is comparable to the previous DFT-calculated work function values of the SrVO-terminated (110) SrVO3 surface (2.3 eV) and SrO-terminated (100) surface (1.9 eV). The measured 2.0 eV value is also much lower than the work function for the (001) LaB6 single crystal cathode (∼2.7 eV) and comparable to the effective work function of B-type dispenser cathodes (∼2.1 eV). If SrVO3 thermionic emitters can be engineered to access domains of this low 2.0 eV work function, they have the potential to significantly improve thermionic emitter-based technologies.

There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work, we propose the method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data’s correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data, we find precision and recall both close to 90% from the best conversational LLMs, like GPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to , due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using .

In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from ab initio calculations in amorphous structures. The local feature representation is specifically chosen to make the linear weights a force field as a force/potential function of the atom pair distance. Consequently, this set of functions is the closest representation of the ab initio forces, given the two-body approximation and finite scanning in the configurational space. We validate this approach in amorphous silica. Potentials in the new force field (consisting of tabulated Si–Si, Si–O, and O–O potentials) are significantly different than existing potentials that are commonly used for silica, even though all of them produce the tetrahedral network structure and roughly similar glass properties. This suggests that the commonly used classical force fields do not offer fundamentally accurate representations of the atomic interaction in silica. The new force field furthermore produces a lower glass transition temperature (Tg ∼ 1800 K) and a positive liquid thermal expansion coefficient, suggesting the extraordinarily high Tg and negative liquid thermal expansion of simulated silica could be artifacts of previously developed classical potentials. Overall, the proposed approach provides a fundamental yet intuitive way to evaluate two-body potentials against ab initio calculations, thereby offering an efficient way to guide the development of classical force fields.

Single phase, polycrystalline perovskite oxide SrVO3, with its intrinsic low effective work function and facile synthesis process, is a promising thermionic electron emitter cathode candidate, in which previous works have shown evidence of an effective work function as low as 2.3 eV. In this work, we study the vacuum activation process of SrVO3 and find that it has promising emission stability over 15 days of continuous high temperature operation. We find that SrVO3 shows surface Sr and O segregation during its operation, which we hypothesize is needed to create a positive surface dipole, leading to a low effective work function. Emission repeatability from cyclic heating and cooling suggests the promising stability of the low effective work function surface, and additional observations of drift-free emission during 1 h of continuous emission testing at high temperature further demonstrate its excellent performance stability. This assessment of the emission stability over time and the interplay of evolving surface chemistry with emission behavior are necessary for understanding how best to prepare, process, and operate SrVO3 cathodes.

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets.

This study presents an efficient language model-based method for high-precision data extraction from text, requiring minimal human effort.

Amorphous titanium dioxide (TiO₂) film coating by atomic layer deposition (ALD) is a promising strategy to extend the photoelectrode lifetime to meet the industrial standard for solar fuel generation. To realize this promise, the essential structure-property relationship that dictates the protection lifetime needs to be uncovered. In this work, we reveal that in addition to the imbedded crystalline phase, the presence of residual chlorine (Cl) ligands is detrimental to the silicon (Si) photoanode lifetime. We further demonstrate that post-ALD in-situ water treatment can effectively decouple the ALD reaction completeness from crystallization. The as-processed TiO₂ film has a much lower residual Cl concentration and thus an improved film stoichiometry, while its uniform amorphous phase is well preserved. As a result, the protected Si photoanode exhibits a substantially improved lifetime to ~600 h at a photocurrent density of more than 30 mA/cm². This study demonstrates a significant advancement toward sustainable hydrogen generation.

Amorphous titanium dioxide (TiO₂) film coating by atomic layer deposition (ALD) is a promising strategy to extend the photoelectrode lifetime to meet the industrial standard for solar fuel generation. To realize this promise, the essential structure-property relationship that dictates the protection lifetime needs to be uncovered. In this work, we reveal that in addition to the imbedded crystalline phase, the presence of residual chlorine (Cl) ligands is detrimental to the silicon (Si) photoanode lifetime. We further demonstrate that post-ALD in-situ water treatment can effectively decouple the ALD reaction completeness from crystallization. The as-processed TiO₂ film has a much lower residual Cl concentration and thus an improved film stoichiometry, while its uniform amorphous phase is well preserved. As a result, the protected Si photoanode exhibits a substantially improved lifetime to ~600 h at a photocurrent density of more than 30 mA/cm². This study demonstrates a significant advancement toward sustainable hydrogen generation.

The evolution of medium range ordering (MRO) and crystallization behavior of amorphous TiO2 films grown by atomic layer deposition (ALD) were studied using in situ four-dimensional scanning transmission electron microscopy. The films remain fully amorphous when grown at 120 °C or below, but they start showing crystallization of anatase phases when grown at 140 °C or above. The degree of MRO increases as a function of temperature and maximizes at 140 °C when crystallization starts to occur, which suggests that crystallization prerequires the development of nanoscale MRO that serves as the site of nucleation. In situ annealing of amorphous TiO2 films grown at 80 °C shows enhancement of MRO but limited number of nucleation, which suggests that post-annealing develops only a small portion of MRO into crystal nuclei. The MRO regions that do not develop into crystals undergo structural relaxation instead, which provides insights into the critical size and degree of ordering and the stability of certain MRO types at different temperatures. In addition, crystallographic defects were observed within crystal phases, which likely negate corrosion resistance of the film. Our result highlights the importance of understanding and controlling MRO for optimizing ALD-grown amorphous films for next-generation functional devices and renewable energy applications.

The evolution of medium range ordering (MRO) and crystallization behavior of amorphous TiO2 films grown by atomic layer deposition (ALD) were studied using in situ four-dimensional scanning transmission electron microscopy. The films remain fully amorphous when grown at 120 °C or below, but they start showing crystallization of anatase phases when grown at 140 °C or above. The degree of MRO increases as a function of temperature and maximizes at 140 °C when crystallization starts to occur, which suggests that crystallization prerequires the development of nanoscale MRO that serves as the site of nucleation. In situ annealing of amorphous TiO2 films grown at 80 °C shows enhancement of MRO but limited number of nucleation, which suggests that post-annealing develops only a small portion of MRO into crystal nuclei. The MRO regions that do not develop into crystals undergo structural relaxation instead, which provides insights into the critical size and degree of ordering and the stability of certain MRO types at different temperatures. In addition, crystallographic defects were observed within crystal phases, which likely negate corrosion resistance of the film. Our result highlights the importance of understanding and controlling MRO for optimizing ALD-grown amorphous films for next-generation functional devices and renewable energy applications.

The information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are high performance, computationally efficient methods to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models.

Accurately quantifying swelling of alloys that have undergone irradiation is essential for understanding alloy performance in a nuclear reactor and critical for the safe and reliable operation of reactor facilities. However, typical practice is for radiation-induced defects in electron microscopy images of alloys to be manually quantified by domain-expert researchers. Here, we employ an end-to-end deep learning approach using the Mask Regional Convolutional Neural Network (Mask R-CNN) model to detect and quantify nanoscale cavities in irradiated alloys. We have assembled a database of labeled cavity images which includes 400 images, > 34 k discrete cavities, and numerous alloy compositions and irradiation conditions. We have evaluated both statistical (precision, recall, and F1 scores) and materials property-centric (cavity size, density, and swelling) metrics of model performance, and performed targeted analysis of materials swelling assessments. We find our model gives assessments of material swelling with an average (standard deviation) swelling mean absolute error based on random leave-out cross-validation of 0.30 (0.03) percent swelling. This result demonstrates our approach can accurately provide swelling metrics on a per-image and per-condition basis, which can provide helpful insight into material design (e.g., alloy refinement) and impact of service conditions (e.g., temperature, irradiation dose) on swelling. Finally, we find there are cases of test images with poor statistical metrics, but small errors in swelling, pointing to the need for moving beyond traditional classification-based metrics to evaluate object detection models in the context of materials domain applications.

Short-timescale atomic rearrangements are fundamental to the kinetics of glasses and frequently dominated by one atom moving significantly (a rearrangement), while others relax only modestly. The rates and directions of such rearrangements (or hops) are dominated by the distributions of activation barriers (Eact) for rearrangement for a single atom and how those distributions vary across the atoms in the system. We have used molecular dynamics simulations of Cu50Zr50 metallic glass below Tg in an isoconfigurational ensemble to catalog the ensemble of rearrangements from thousands of sites. The majority of atoms are strongly caged by their neighbors, but a tiny fraction has a very high propensity for rearrangement, which leads to a power-law variation in the cage-breaking probability for the atoms in the model. In addition, atoms generally have multiple accessible rearrangement vectors, each with its own Eact. However, atoms with lower Eact (or higher rearrangement rates) generally explored fewer possible rearrangement vectors, as the low Eact path is explored far more than others. We discuss how our results influence future modeling efforts to predict the rearrangement vector of a hopping atom.

Engineering a material's work function is of central importance for many technologies and in particular electron emitters used in high‐power vacuum electronics and thermionic energy converters. A low work function surface is typically achieved through unstable surface functional species, especially in high power thermionic electron emitter applications. Discovering and engineering new materials with intrinsic, stable low work functions obtainable without volatile surface species would mark a definitive advancement in the design of electron emitters. This work reports evidence for the existence of a low work function surface on a bulk, monolithic, electrically conductive perovskite oxide: SrVO₃. After considering the patch field effect on the heterogeneous emitting surface of the bulk polycrystalline samples, this study suggests the presence of low work function (≈2 eV) emissive grains on SrVO₃ surface. Emission current densities of 10–100 mA cm^–2 at ≈1000 °C, comparable to commercial LaB₆ thermionic cathodes, indicative of an overall effective thermionic work function of 2.3–2.7 eV are obtained. This study demonstrates that perovskites like SrVO₃ may have intrinsically low work functions comparable to commercialized W‐based dispenser cathodes and suggests that, with further engineering, perovskites may represent a new class of low work function electron emitters.

Engineering a material's work function is of central importance for many technologies and in particular electron emitters used in high‐power vacuum electronics and thermionic energy converters. A low work function surface is typically achieved through unstable surface functional species, especially in high power thermionic electron emitter applications. Discovering and engineering new materials with intrinsic, stable low work functions obtainable without volatile surface species would mark a definitive advancement in the design of electron emitters. This work reports evidence for the existence of a low work function surface on a bulk, monolithic, electrically conductive perovskite oxide: SrVO₃. After considering the patch field effect on the heterogeneous emitting surface of the bulk polycrystalline samples, this study suggests the presence of low work function (≈2 eV) emissive grains on SrVO₃ surface. Emission current densities of 10–100 mA cm^–2 at ≈1000 °C, comparable to commercial LaB₆ thermionic cathodes, indicative of an overall effective thermionic work function of 2.3–2.7 eV are obtained. This study demonstrates that perovskites like SrVO₃ may have intrinsically low work functions comparable to commercialized W‐based dispenser cathodes and suggests that, with further engineering, perovskites may represent a new class of low work function electron emitters.

Discovering and engineering new materials with fast oxygen surface exchange kinetics and robust long‐term stability is essential for the large‐scale, economically viable commercialization of solid oxide fuel cell (SOFC) technology. The perovskite catalyst material BaFe_0.125Co_0.125Zr_0.75O₃ (BFCZ75), predicted to be promising from recent density functional theory (DFT) calculations and unconventional due to its extremely high Zr content and low electronic conductivity, exhibits oxygen reduction reaction surface exchange rates on par with Ba_0.5Sr_0.5Co_0.8Fe_0.2O₃ (BSCF) and excellent stability at typical operating temperatures. New composite electrodes are engineered by integrating BFCZ75 with commercial electrode materials La_1–xSr_xMnO₃ (LSM) and La_1–xSr_xCo_yFe_1–yO₃ (LSCF) and achieve high performance as measured by low area specific resistance (ASR) values, with the LSCF/BFCZ75 ASR values comparable to top performing noncomposite electrode materials such as SrCo_0.8Sc_0.2O_3–δ, BaNb_0.05Fe_0.95O_3–δ and BaCo_0.7Fe_0.22Y_0.08O_3–δ. The use of BFCZ75 as a composite with LSCF achieving low ASR values shows that BFCZ75 is highly active and can easily integrate into existing SOFC material supply chains, lowering the barrier for potential commercial application of new electrode materials. Finally, these findings point to a broader unexplored class of perovskite materials with high fractions of redox inactive species (e.g., Zr, Nb, and Ta) that may unlock new pathways to realizing improved commercial SOFCs.

Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted. A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models, which are often generated by the generally applicable bootstrap method. In this work, we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty but that it can be simply calibrated to dramatically improve its accuracy. We demonstrate the effectiveness of this calibration method for both synthetic data and numerous physical datasets from the field of Materials Science and Engineering. The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models.

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

There is an ongoing effort to replace rare and expensive noble‐element catalysts with more abundant and less expensive transition metal oxides. With this goal in mind, the intrinsic defects of a rhombohedral perovskite‐like structure of LaMnO₃ and their implications on CO catalytic properties were studied. Surface thermodynamic stability as a function of pressure (P) and temperature (T) were calculated to find the most stable surface under reaction conditions (P=0.2 atm, T=323 K to 673 K). Crystallographic planes (100), (111), (110), and (211) were evaluated and it was found that (110) with MnO₂ termination was the most stable under reaction conditions. Adsorption energies of O₂ and CO on (110) as well as the effect of intrinsic defects such as Mn and O vacancies were also calculated. It was found that O vacancies favor the interaction of CO on the surface, whereas Mn vacancies can favor the formation of carbonate species.

Transition metal dichalcogenides (TMDs), especially in two-dimensional (2D) form, exhibit many properties desirable for device applications. However, device performance can be hindered by the presence of defects. Here, we combine state of the art experimental and computational approaches to determine formation energies and charge transition levels of defects in bulk and 2D MX₂ (M = Mo or W; X = S, Se, or Te). We perform deep level transient spectroscopy (DLTS) measurements of bulk TMDs. Simultaneously, we calculate formation energies and defect levels of all native point defects, which enable identification of levels observed in DLTS and extend our calculations to vacancies in 2D TMDs, for which DLTS is challenging. We find that reduction of dimensionality of TMDs to 2D has a significant impact on defect properties. This finding may explain differences in optical properties of 2D TMDs synthesized with different methods and lays foundation for future developments of more efficient TMD-based devices.

Medium-range ordering within the amorphous TiO₂ thin film is revealed by 4-D STEM and the atomic configuration is determined by multi-objective structure optimization StructOpt guided by experimental data and theoretical constraints.

Irradiation increases the yield stress and embrittles light water reactor (LWR) pressure vessel steels. In this study, we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux, high fluence, extended life conditions. The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence, plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations, up to very high fluence. Notably, the machine learning model predictions for the high fluence, intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models. The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence. Similar approaches, applied to expanded databases, could be used to predict hardening in LWRs under life-extension conditions.

Aqueous organic redox flow batteries (AORFBs) hold great promise for safe, sustainable, and cost-effective grid energy storage. However, developing catholyte redox molecules with desired energy density, power, and stability simultaneously has long been a critical challenge for AORFBs. Here, we report a novel class of ionic liquid mimicking TEMPO dimers (i-TEMPODs) that can be produced by our newly developed building block assembly synthetic platform. By systematically investigating 21 derivatives, we reveal i-TEMPODs have optimized size and charge that is compatible with highly conductive membrane and can form a “water-in-catholyte” (WiC) state. The tight coordination dynamics with water molecules deliver extreme solubility with promoted electrochemical stability at highly positive potentials. Leveraging these advances, we identify a champion molecule and demonstrate record overall AORFB performance in energy density (47.3 Wh/L), power density (0.325 W/cm2), and stability (no apparent capacity decay after 96 days) with low-cost and scalable chemistry.

Recent machine learning models for bandgap prediction that explicitly encode the structure information to the model feature set significantly improve the model accuracy compared to both traditional machine learning and non-graph-based deep learning methods. The ongoing rapid growth of open-access bandgap databases can benefit such model construction not only by expanding their domain of applicability but also by requiring constant updating of the model. Here, we build a new state-of-the-art multi-fidelity graph network model for bandgap prediction of crystalline compounds from a large bandgap database of experimental and density functional theory (DFT) computed bandgaps with over 806 600 entries (1500 experimental, 775 700 low-fidelity DFT, and 29 400 high-fidelity DFT). The model predicts bandgaps with a 0.23 eV mean absolute error in cross validation for high-fidelity data, and including the mixed data from all different fidelities improves the prediction of the high-fidelity data. The prediction error is smaller for high-symmetry crystals than for low symmetry crystals. Our data are published through a new cloud-based computing environment, called the “Foundry,” which supports easy creation and revision of standardized data structures and will enable cloud accessible containerized models, allowing for continuous model development and data accumulation in the future.

Secondary electron yield (SEY) is relevant for widely used characterization methods (e.g., secondary electron spectroscopy and electron microscopy) and materials applications (e.g., multipactor effect). Key quantities necessary for understanding the physics of electron transport in materials and simulation of SEY are electron mean free paths (MFPs). This paper explores the impact of alloying on MFPs and SEY for Cu-Ni, Cu-Zn, and Mo-Li alloys relative to their component metals Cu, Ni, Zn, Mo, and Li. Density functional theory calculations yield density of states, Fermi energy, work function, and frequency- and momentum-dependent energy loss function. These material properties were used to calculate MFPs and Monte Carlo simulations were performed to obtain energy dependent SEY for the alloys as well for the component metals. The results show that MFPs and SEYs of the studied alloys lie between those of component pure elements but are not a simple composition weighted average. Detailed analysis of the secondary electron generation and emission process shows that the changes in the SEY of alloys relative to the SEY of their component metals depend on the changes in both electronic structure and dielectric properties of the material.

The enhancement of surface diffusion (DS) over the bulk (DV) in metallic glasses (MGs) is well documented and likely to strongly influence the properties of glasses grown by vapor deposition. Here, we use classical molecular dynamics (MD) simulations to identify different factors influencing the enhancement of surface diffusion in MGs. MGs have a simple atomic structure and belong to the category of moderately fragile glasses that undergo pronounced slowdown of bulk dynamics with cooling close to the glass transition temperature (Tg). We observe that DS exhibits a much more moderate slowdown compared to DV when approaching Tg, and DS/DV at Tg varies by two orders of magnitude among the MGs investigated. We demonstrate that both the surface energy and the fraction of missing bonds for surface atoms show good correlation to DS/DV, implying that the loss of nearest neighbors at the surface directly translates into higher mobility, unlike the behavior of network-bonded and hydrogen-bonded organic glasses. Fragility, a measure of the slowdown of bulk dynamics close to Tg, also correlates to DS/DV, with more fragile systems having larger surface enhancement of mobility. The deviations observed in the fragility–DS/DV relationship are shown to be correlated to the extent of segregation or depletion of the mobile element at the surface. Finally, we explore the relationship between the diffusion pre-exponential factor (D0) and the activation energy (Q) and compare it to a ln(D0)–Q correlation previously established for bulk glasses, demonstrating similar correlations from MD as in the experiments and that the surface and bulk have very similar ln(D0)–Q correlations.

High throughput DFT simulations yield 7 low work function perovskites as promising cathode materials.

The work function is one of the most fundamental surface properties of a material, and understanding and controlling its value is of central importance for manipulating electron flow in applications ranging from high power vacuum electronics to oxide electronics and solar cells. Recent computational studies using Density Functional Theory (DFT) have demonstrated that DFT-calculated work function values for metals tend to agree well (within about 0.3 eV on average) with experimental values. However, a detailed validation of DFT-calculated work functions for oxide materials has not been conducted and is challenging due to the complex dipole structures that can occur on oxide surfaces. In this work, we have focused our investigation on the widely studied perovskite SrTiO3 as a case study example. We find that DFT can accurately predict the work function values of clean and reconstructed SrTiO3 surfaces vs experiment at about the same level of accuracy as metals when direct comparisons can be made. Furthermore, to aid in understanding the factors governing the work function of oxides, we have performed systematic studies on the influence of common surface features, including surface point defects, doping, adsorbates, reconstructions, and surface steps, on the work function. The relationships between the surface structure and work function for SrTiO3 identified here may be qualitatively applicable to other complex oxide materials.

The work function is one of the most fundamental surface properties of a material, and understanding and controlling its value is of central importance for manipulating electron flow in applications ranging from high power vacuum electronics to oxide electronics and solar cells. Recent computational studies using Density Functional Theory (DFT) have demonstrated that DFT-calculated work function values for metals tend to agree well (within about 0.3 eV on average) with experimental values. However, a detailed validation of DFT-calculated work functions for oxide materials has not been conducted and is challenging due to the complex dipole structures that can occur on oxide surfaces. In this work, we have focused our investigation on the widely studied perovskite SrTiO3 as a case study example. We find that DFT can accurately predict the work function values of clean and reconstructed SrTiO3 surfaces vs experiment at about the same level of accuracy as metals when direct comparisons can be made. Furthermore, to aid in understanding the factors governing the work function of oxides, we have performed systematic studies on the influence of common surface features, including surface point defects, doping, adsorbates, reconstructions, and surface steps, on the work function. The relationships between the surface structure and work function for SrTiO3 identified here may be qualitatively applicable to other complex oxide materials.

Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure‐property relationship (QSPR) analysis to effectively predict flash points. In recent years graph‐based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, message‐passing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing against 12 previous QSPR studies using more traditional methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3 % lower and 2.0 K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.

Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.

Labradorite feldspars of the plagioclase solid solution series have been known for their complicated subsolidus phase relations and enigmatic incommensurately modulated structures. Characterized by the irrationally indexede-reflections in the diffraction pattern,e-labradorite shows the largest variation in the incommensurate ordering states among thee-plagioclase structures. The strongly ordered low-temperaturee-labradorite is one of the last missing pieces of thee-plagioclase puzzle. Nine plutonic and metamorphic labradorite feldspar samples from Canada, Ukraine, Minnesota (USA), Tanzania and Greenland with compositions ranging from An_52.5to An₆₈were studied with single-crystal X-ray diffraction. Two crystals from Labrador, Canada, and Duluth, MN, USA, with wide enough twin lamellae were analyzed with single-crystal neutron diffraction. The incommensurately modulated structures ofe-plagioclase are refined for the first time with neutron diffraction data, which confirmed that the T—O distance modulation in the low-temperaturee-plagioclase results from the Al–Si ordering in the framework. Detailed configurations of the M site are also observed in the structures refined from neutron diffraction data, which were not possible to see with X-ray diffraction data. The relation between theq-vectors and the mole% An composition is revealed for the entire compositional range ofe-plagioclase, from An₂₅to An₇₅. The previously proposed two-trend relation depending on the cooling rate and phase transition path is confirmed. A new classification ofe-plagioclase (e_α,e_βande_γ) is proposed based on theq-vector of the structure, which makes it an independent character from the presence/absence of density modulation. New parameters are proposed to quantify the ordering states of these complicated aperiodic structures ofe-plagioclases, such as the difference between 〈T₁o—O〉 and 〈T₁m—O〉 at phaset= 0.2 or the normalized intensity of the (071\bar 1) reflection.

Accurate quantum mechanical scanning transmission electron microscopy image simulation methods such as the multislice method require computation times that are too large to use in applications in high-resolution materials imaging that require very large numbers of simulated images. However, higher-speed simulation methods based on linear imaging models, such as the convolution method, are often not accurate enough for use in these applications. We present a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correct it. We develop and validate this method using simulated images of Pt and Pt–Mo nanoparticles and find that for these systems, once the polynomial is fit, the method runs about six orders of magnitude faster than parallelized CPU implementations of the multislice method while achieving a 1 − R² error of 0.010–0.015 and root-mean-square error/standard deviation of dataset being predicted of about 0.1 when compared to full multislice simulations.

Atom probe tomography (APT) and scanning transmission electron microscopy (STEM) techniques were used to probe the long-time thermal stability of nm-scale Mn-Ni-Si precipitates (MNSPs) formed in intermediate and high Ni reactor pressure vessel steels under high fluence neutron irradiation at ≈320 °C. Post irradiation annealing (PIA) at 425 °C for up to 57 weeks was used to determine if the MNSPs are: (a) non-equilibrium solute clusters formed and sustained by radiation induced segregation (RIS); or, (b) equilibrium G or Γ₂ phases, that precipitate at accelerated rates due to radiation enhanced diffusion (RED). Note the latter is consistent with both thermodynamic models and x-ray diffraction (XRD) measurements. Both the experimental and an independently calibrated cluster dynamics (CD) model results show that the stability of the MNSPs is very sensitive to the alloy Ni and, to a lesser extent, Mn content. Thus, a small fraction of the largest MNSPs in the high Ni steel persist, and begin to coarsen at long times. These results suggest that the MNSPs remain a stable phase, even at 105 °C higher than they formed at, thus are most certainly equilibrium phases at much lower service relevant temperatures of ≈290 °C.

The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

Reducing the working temperature of solid oxide fuel cells is critical to their increased commercialization but is inhibited by the slow oxygen exchange kinetics at the cathode, which limits the overall rate of the oxygen reduction reaction. We use ab initio methods to develop a quantitative elementary reaction model of oxygen exchange in a representative cathode material, La_0.5Sr_0.5CoO_3−δ, and predict that under operating conditions the rate-limiting step for oxygen incorporation from O₂ gas on the stable, (001)-SrO surface is lateral (surface) diffusion of O-adatoms and oxygen surface vacancies. We predict that a high vacancy concentration on the metastable CoO₂ termination enables a vacancy-assisted O₂ dissociation that is 10²–10³ times faster than the rate limiting step on the Sr-rich (La,Sr)O termination. This result implies that dramatically enhanced oxygen exchange performance could potentially be obtained by suppressing the (La,Sr)O termination and stabilizing highly active CoO₂ termination.

Two critical limitations of organic–inorganic lead halide perovskite materials for solar cells are their poor stability in humid environments and inclusion of toxic lead. In this study, high‐throughput density functional theory (DFT) methods are used to computationally model and screen 1845 halide perovskites in search of new materials without these limitations that are promising for solar cell applications. This study focuses on finding materials that are comprised of nontoxic elements, stable in a humid operating environment, and have an optimal bandgap for one of single junction, tandem Si‐perovskite, or quantum dot–based solar cells. Single junction materials are also screened on predicted single junction photovoltaic (PV) efficiencies exceeding 22.7%, which is the current highest reported PV efficiency for halide perovskites. Generally, these methods qualitatively reproduce the properties of known promising nontoxic halide perovskites that are either experimentally evaluated or predicted from theory. From a set of 1845 materials, 15 materials pass all screening criteria for single junction cell applications, 13 of which are not previously investigated, such as (CH₃NH₃)_0.75Cs_0.25SnI₃, ((NH₂)₂CH)Ag_0.5Sb_0.5Br₃, CsMn_0.875Fe_0.125I₃, ((CH₃)₂NH₂)Ag_0.5Bi_0.5I₃, and ((NH₂)₂CH)_0.5Rb_0.5SnI₃. These materials, together with others predicted in this study, may be promising candidate materials for stable, highly efficient, and nontoxic perovskite‐based solar cells.

Four basaltic phenocryst samples of plagioclase, with compositions ranging from An₄₈(andesine) to An₆₄(labradorite), have been studied with single-crystal X-ray and neutron diffraction techniques. The samples were also subjected to a heating experiment at 1100°C for two weeks in an effort to minimize the Al–Si ordering in their structures. The average and the modulated structures of the samples (before and after the heating experiment) were compared, in order to understand the mechanism of the phase transition from the disordered C\bar 1 structure to thee-plagioclase structure. A comparison between the structures from neutron and X-ray diffraction data shows that the 〈T—O〉 distance does not solely depend on the Al occupancy as previously thought. A dramatic decrease of the Al–Si ordering is observed after heating at 1100°C for two weeks for all four samples, with an obvious change in the intensities of the satellite reflections (e-reflections) in the diffraction pattern. Evident changes in the modulation period were also observed for the more calcic samples. No obvious change in the Ca–Na ordering was observed after the heating experiment. Anin situheating X-ray diffraction experiment was carried out on the andesine sample (An₄₈) to study the change in the satellite intensity at high temperature. A dramatic weakening of the satellite peaks was observed between 477°C and 537°C, which strongly supports the displacive nature of the initiation ofe2 ordering. Rigid-Unit Mode (RUM) analysis of the plagioclase structure suggests the initial position of thee-reflections is determined by the anti-RUMs in the framework.

Valleyite, Ca4(Fe,Al)6O13, is a new sodalite-type mineral discovered in late Pleistocene basaltic scoria from the Menan Volcanic Complex near Rexburg, Idaho, U.S.A. It is an oxidation product of basaltic glass during the early stage of the scoria formation and is associated with hematite (α-Fe2O3), maghemite (γ-Fe2O3), luogufengite (ε-Fe2O3), and quartz on the surface of vesicles. The measured crystal size of valleyite ranges from ~250 to ~500 nm. The empirical chemical formula of valleyite is (Ca3.61Mg0.39)(Fe3.97Al1.91Ti0.09)O13. The mineral has a space group of I43m. The (Fe,Al)-O bond distance and unit-cell edge are slightly larger than those reported for synthetic Ca4Al6O13, presumably due to the presence of the larger Fe3+ cations, compared with Al3+, in the structure. Density functional theory calculations predict that valleyite may be a metastable phase at low temperatures. Measured Curie temperatures for valleyite and luogufengite are 645 and 519 K, respectively. Their magnetization hysteresis loop indicates the magnetic exchange coupling between valleyite (soft magnet) and luogufengite (hard magnet) that aids in the understanding of magnetic properties and paleo-magnetism of basaltic rocks. This new mineral, valleyite, with the sodalite-type cage structure is potentially a functional magnetic material.

Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison–based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques.

Oxygen defect control has long been considered an important route to functionalizing complex oxide films. However, the nature of oxygen defects in thin films is often not investigated beyond basic redox chemistry. One of the model examples for oxygen-defect studies is the layered Ruddlesden–Popper phase La_2−xSr _x CuO_4−δ (LSCO), in which the superconducting transition temperature is highly sensitive to epitaxial strain. However, previous observations of strain-superconductivity coupling in LSCO thin films were mainly understood in terms of elastic contributions to mechanical buckling, with minimal consideration of kinetic or thermodynamic factors. Here, we report that the oxygen nonstoichiometry commonly reported for strained cuprates is mediated by the strain-modified surface exchange kinetics, rather than reduced thermodynamic oxygen formation energies. Remarkably, tensile-strained LSCO shows nearly an order of magnitude faster oxygen exchange rate than a compressively strained film, providing a strategy for developing high-performance energy materials.

Oxygen defect control has long been considered an important route to functionalizing complex oxide films. However, the nature of oxygen defects in thin films is often not investigated beyond basic redox chemistry. One of the model examples for oxygen-defect studies is the layered Ruddlesden–Popper phase La_2−xSr _x CuO_4−δ (LSCO), in which the superconducting transition temperature is highly sensitive to epitaxial strain. However, previous observations of strain-superconductivity coupling in LSCO thin films were mainly understood in terms of elastic contributions to mechanical buckling, with minimal consideration of kinetic or thermodynamic factors. Here, we report that the oxygen nonstoichiometry commonly reported for strained cuprates is mediated by the strain-modified surface exchange kinetics, rather than reduced thermodynamic oxygen formation energies. Remarkably, tensile-strained LSCO shows nearly an order of magnitude faster oxygen exchange rate than a compressively strained film, providing a strategy for developing high-performance energy materials.

Critical to the development of improved solid oxide fuel cell (SOFC) technology are novel compounds with high oxygen reduction reaction (ORR) catalytic activity and robust stability under cathode operating conditions. Approximately 2145 distinct perovskite compositions are screened for potential use as high activity, stable SOFC cathodes, and it is verified that the screening methodology qualitatively reproduces the experimental activity, stability, and conduction properties of well‐studied cathode materials. The calculated oxygen p‐band center is used as a first principle‐based descriptor of the surface exchange coefficient (k*), which in turn correlates with cathode ORR activity. Convex hull analysis is used under operating conditions in the presence of oxygen, hydrogen, and water vapor to determine thermodynamic stability. This search has yielded 52 potential cathode materials with good predicted stability in typical SOFC operating conditions and predicted k* on par with leading ORR perovskite catalysts. The established trends in predicted k* and stability are used to suggest methods of improving the performance of known promising compounds. The material design strategies and new materials discovered in the computational search help enable the development of high activity, stable compounds for use in future solid oxide fuel cells and related applications.

Materials that exhibit a low work function and therefore easily emit electrons into vacuum form the basis of electronic devices used in applications ranging from satellite communications to thermionic energy conversion. W–Ba–O is the canonical materials system that functions as the thermionic electron emitter commercially used in a range of high-power electron devices. However, the work functions, surface stability, and kinetic characteristics of a polycrystalline W emitter surface are still not well understood or characterized. In this study, we examined the work function and surface stability of the eight lowest index surfaces of the W–Ba–O system using density functional theory methods. We found that under the typical thermionic cathode operating conditions of high temperature and low oxygen partial pressure, the most stable surface adsorbates are Ba–O species with compositions in the range of Ba0.125O–Ba0.25O per surface W atom, with O passivating all dangling W bonds and Ba creating work function-lowering surface dipoles. Wulff construction analysis reveals that the presence of O and Ba significantly alters the surface energetics and changes the proportions of surface facets present under equilibrium conditions. Analysis of previously published data on W sintering kinetics suggests that fine W particles in the size range of 100-500 nm may be at or near equilibrium during cathode synthesis and thus may exhibit surface orientation fractions well described by the calculated Wulff construction.

Cesium (Cs) is a radioactive fission product whose release is of concern for Tristructural-Isotropic fuel particles. In this work, Cs diffusion through high energy grain boundaries (HEGBs) of cubic-SiC is studied using an ab-initio based kinetic Monte Carlo (kMC) model. The HEGB environment was modeled as an amorphous SiC, and Cs defect energies were calculated using the density functional theory (DFT). From defect energies, it was suggested that the fastest diffusion mechanism is the diffusion of Cs interstitial in an amorphous SiC. The diffusion of Cs interstitial was simulated using a kMC model, based on the site and transition state energies sampled from the DFT. The Cs HEGB diffusion exhibited an Arrhenius type diffusion in the range of 1200–1600 °C. The comparison between HEGB results and the other studies suggests not only that the GB diffusion dominates the bulk diffusion but also that the HEGB is one of the fastest grain boundary paths for the Cs diffusion. The diffusion coefficients in HEGB are clearly a few orders of magnitude lower than the reported diffusion coefficients from in- and out-of-pile samples, suggesting that other contributions are responsible, such as radiation enhanced diffusion.

A continuum model for calculating the time-dependent hydrogen pickup fractions in various Zirconium alloys under steam and pressured water oxidation has been developed in this study. Using only one fitting parameter, the effective hydrogen gas partial pressure at the oxide surface, a qualitative agreement is obtained between the predicted and previously measured hydrogen pickup fractions. The calculation results therefore demonstrate that H diffusion through the dense oxide layer plays an important role in the hydrogen pickup process. The limitations and possible improvement of the model are also discussed.

Earth's lower mantle is generally believed to be seismically and chemically homogeneous because most of the key seismic parameters can be explained using a simplified mineralogical model at expected pressure‐temperature conditions. However, recent high‐resolution tomographic images have revealed seismic and chemical stratification in the middle‐to‐lower parts of the lower mantle. Thus far, the mechanism for the compositional stratification and seismic inhomogeneity, especially their relationship with the speciation of iron in the lower mantle, remains poorly understood. We have built a complete and integrated thermodynamic model of iron and aluminum chemistry for lower mantle conditions and from this model has emerged a stratified picture of the valence, spin, and composition profile in the lower mantle. Within this picture the lower mantle has an upper region with Fe³⁺‐enriched bridgmanite with high‐spin ferropericlase and metallic Fe and a lower region with low‐spin, iron‐enriched ferropericlase coexisting with iron‐depleted bridgmanite and almost no metallic Fe. The transition between the regions occurs at a depth of around 1600 km and is driven by the spin transition in ferropericlase, which significantly changes the iron partitioning and speciation to one that favors Fe²⁺ in ferropericlase and suppresses Fe³⁺ and metallic iron formation. These changes lead to lowered bulk sound velocity by 3–4% around the middle‐lower mantle and enhanced density by ~1% toward the lowermost mantle. The predicted chemically and seismically stratified lower mantle differs dramatically from the traditional homogeneous model.

The 3D atomic structure of an interface‐stabilized planar boundary in the magnetoelectric Cr₂O₃ thin films is reported based on scanning transmission electron microscopy as a function of scattering angle. Local boundary electron energy loss spectroscopy shows a prepeak on the O–K edge arising from unoccupied O 2p states. Density functional theory calculations reproduce the images and spectra and show that the boundary has smaller bandgap than bulk Cr₂O₃, but does not interrupt the (0001) surface spin polarization and boundary magnetization. The reduced bandgap at the boundaries means they provide potential breakdown paths in Cr₂O₃ thin films. The same planar defect is predicted to occur in other epitaxial corundum film/substrate systems.

This paper reports an unexpected change in the oxidation state of Fe in bridgmanite, the most dominant mineral in the lower mantle. The oxidation state change resolves the discrepancy between laboratory and seismic studies on the chemical composition of the lower mantle, showing that the lower mantle has major element chemistry similar to the upper mantle. The oxidation state change will also lead to a lower Fe content in bridgmanite in the midmantle, whereas the total Fe content remains the same. Such a change can lead to an increase in viscosity at 1,100- to 1,700-km depths, providing a viable mineralogical explanation on possible viscosity elevation suggested by geophysical studies at the same depth range.

Cathode degradation is a key factor that limits the lifetime of Li-ion batteries. To identify functional coatings that can suppress this degradation, we present a high-throughput density functional theory based framework which consists of reaction models that describe thermodynamic and electrochemical stabilities, and acid-scavenging capabilities of materials. Screening more than 130,000 oxygen-bearing materials, we suggest physical and hydrofluoric-acid barrier coatings such as WO₃, LiAl₅O₈ and ZrP₂O₇ and hydrofluoric-acid scavengers such as Sc₂O₃, Li₂CaGeO₄, LiBO₂, Li₃NbO₄, Mg₃(BO₃)₂ and Li₂MgSiO₄. Using a design strategy to find the thermodynamically optimal coatings for a cathode, we further present optimal hydrofluoric-acid scavengers such as Li₂SrSiO₄, Li₂CaSiO₄ and CaIn₂O₄ for the layered LiCoO₂, and Li₂GeO₃, Li₄NiTeO₆ and Li₂MnO₃ for the spinel LiMn₂O₄ cathodes. These coating materials have the potential to prolong the cycle-life of Li-ion batteries and surpass the performance of common coatings based on conventional materials such as Al₂O₃, ZnO, MgO or ZrO₂.

Significant efficiency droop is a major concern for light-emitting diodes and laser diodes operating at high current density. Recent study has suggested that heavily Bi-alloyed GaAs can decrease the non-radiative Auger recombination and therefore alleviate the efficiency droop. Using density functional theory, we studied a newly fabricated quaternary alloy, GaAs1-x-yPyBix, which can host significant amounts of Bi, through calculations of its band gap, spin-orbit splitting, and band offsets with GaAs. We found that the band gap changes of GaAs1-x-yPyBix relative to GaAs are determined mainly by the local structural changes around P and Bi atoms rather than their electronic structure differences. To obtain alloy with lower Auger recombination than GaAs bulk, we identified the necessary constraints on the compositions of P and Bi. Finally, we demonstrated that GaAs/GaAs1-x-yPyBix heterojunctions with potentially low Auger recombination can exhibit small lattice mismatch and large enough band offsets for strong carrier confinement. This work shows that the electronic properties of GaAs1-x-yPyBix are potentially suitable for high-power infrared light-emitting diodes and laser diodes with improved efficiency.

Significant efficiency droop is a major concern for light-emitting diodes and laser diodes operating at high current density. Recent study has suggested that heavily Bi-alloyed GaAs can decrease the non-radiative Auger recombination and therefore alleviate the efficiency droop. Using density functional theory, we studied a newly fabricated quaternary alloy, GaAs1-x-yPyBix, which can host significant amounts of Bi, through calculations of its band gap, spin-orbit splitting, and band offsets with GaAs. We found that the band gap changes of GaAs1-x-yPyBix relative to GaAs are determined mainly by the local structural changes around P and Bi atoms rather than their electronic structure differences. To obtain alloy with lower Auger recombination than GaAs bulk, we identified the necessary constraints on the compositions of P and Bi. Finally, we demonstrated that GaAs/GaAs1-x-yPyBix heterojunctions with potentially low Auger recombination can exhibit small lattice mismatch and large enough band offsets for strong carrier confinement. This work shows that the electronic properties of GaAs1-x-yPyBix are potentially suitable for high-power infrared light-emitting diodes and laser diodes with improved efficiency.

We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. We apply a correction method for solute diffusion in alloys using experimental and simulated values of host self-diffusivity. We find good agreement with experimental solute diffusion data, obtaining a weighted activation barrier RMS error of 0.176 eV when excluding magnetic solutes in non-magnetic alloys. The compiled database is the largest collection of consistently calculated ab-initio solute diffusion data in the world.

In order to better understand electrolyte degradation at cathode-electrolyte interfaces and mechanisms by which coatings can protect against such degradation, it is important to determine to what extent capacity fade occurs predominantly at highly reactive sites vs. uniformly over the cathode surface. To explore this question we have developed an atomic layer deposition (ALD) based thin film coating technique to study the varied effects of LiNi_0.5Mn_0.3Co_0.2O₂ (NMC) surface sites on Li-ion battery charge capacity fade. Small organic molecules selected for their chemical functionality are grafted to the oxide surface to block ALD coating of NMC surface sites of complimentary chemical reactivity to the template molecules. The ALD films are deposited using the well-established Al₂O₃ process, which has been studied extensively in the coating of NMC and similar cathode materials. After ALD, the organic template molecules are removed using ozone treatment, leaving the underlying reactive surface sites of NMC exposed. By exposing different surface sites, it is possible to study their separate effects on battery cycle performance. The protocol for partial ALD coating is established, with screening performed on some candidate template molecules. Both the reproducibility of the grafting, the partial ALD coating, and the template removal are established. The benzylbromide (BB) template molecule will react with hydroxyl groups at the surface, while benzoic acid (BA) will react with basic metal oxide sides. In order to insure the stability of the grafted template molecules, deposition was performed at 100°C, a temperature lower than that employed in a typical ALD process. X-ray photoelectron spectroscopy shows that the BA-NMC surfaces have a higher proportion of Ni exposed on the surface compared to a control sample, while the BB-NMC surface does not have any disproportionate elemental composition. Preliminary results show that despite similar amounts of film deposited, the BA-NMC surface performs better than BB NMC in terms of discharge capacity fade during cycle-performance testing. This preliminary result implies that Ni sites on the NMC surface do not negatively affect cycle-performance. This result is contrary to other recent reports in the field reporting a connection between NiO and solid electrolyte interphase formation, showing further insight into the complex electrochemical phenomena occurring at the cathode surface.

A widely used remedy to slow down the degradation of the electrochemical performances of lithium-ion batteries is to apply coating materials on cathode surfaces.^[1] Such coatings can serve as physical barriers to inhibit electrode-electrolyte side reactions, and they can also possess an additional functionality such as scavenging the detrimental acids (e.g., the hydrofluoric acid, HF) in electrolytes. Following our previous thermodynamics high-throughput search for functional cathode coatings,^[2] we recently further built on our framework for understanding the battery performance improvement associated with coatings by studying and predicting lithium transport through a number of various crystalline and amorphous coating materials using density functional theory (DFT) calculations.

In the current work, we first investigated the Li diffusion in top candidates of crystalline metal oxides from our thermodynamics screening. With the help of MINT (software developed by our group), we found unique Li interstitial sites and corresponding migration pathway in these crystalline metal oxides. After that, DFT based Nudge Elastic Band (NEB) method was applied to calculate the migration barrier and corresponding Li diffusivity for each material was obtained by Kinetic Monte Carlo (KMC) simulation. Since many reports in the literature show coatings are likely to be in an amorphous form, we further studied the Li transport in a variety amorphous oxides and fluorides by methods that combine first principles density functional theory calculations and statistical mechanics.

In above diffusivity calculations, only dilute Li concentrations is taken into account, since at high Li concentration, phase transitions are likely to occur. Knowing that the Li diffusivity can be improved at high Li concentration, we extended our kinetical framework to some Li-contained metal oxides. All these metal oxides, fluorides and Li-contained metal oxides are top coating candidates examined from previous thermodynamic calculations. After all this kinetical calculation of Li transport, a ranking list is predicted and some materials are suggested as good coating materials in terms of Li diffusivity, which is one of the key factors influencing the rate performance of Li ion battery.

This work is one part of a framework for understanding the battery performance improvement associated with coatings and should aid in the future discovery of functional coating materials.

References

[1] Z. Chen, Y. Qin, K. Amine, Y.-K. Sun, J. Mater. Chem. 2010, 20, 7606.

[2] M. Aykol, S. Kirklin, C. Wolverton, Adv. Energy Mater. 2014, 4, 1400690

Acknowledgement

This computational research work was supported by The DOW Chemical Company.

Metal oxide coatings on cathodes can improve electrochemical stability and longer life cycle of rechargeable Li-ion batteries. Such coatings protects the cathode surface from the direct contact with electrolyte, helps to prevent the formation of SEI (solid electrolyte interphase) layer, reduce ionic dissolution and that results in improved capacity retention, columbic efficiency and longer cycle performance ^[1,2]. In literature, several oxides and fluorides coatings such as - Al₂O₃, MgO, TiO₂, ZnO, SnO₂, ZrO₂ and LaF₃,  AlF₃, MgF₂, have been suggested which have demonstrated improved capacity retention ^[3].

Among those coatings, amorphous Al₂O₃ is probably the most effective coating for capacity retention and stability of the cell ^[4]. But Al₂O₃ is a high bandgap insulating material which impose a large resistance on cathode surface resulting smaller charge-discharge capacity at higher C-rates i.e. poor rate performance ^[5]. Therefore, we propose mixed oxides coatings: Al₂O₃-Ga₂O₃, Al₂O₃-MgO and Al₂O₃-ZrO₂ to improve the conductivity of the coatings. The key reason for selectively choosing Ga, Mg and Zr to introduce into Al₂O₃ system is following. As Ga₂O₃ is known for higher electron conductivity, the mixing of Ga should increase the electron conductivity and that can be tuned by varying Ga-content into film. Here Mg, Al, and Zr has valance 2, 3 and 4 respectively, therefore alloying MgO or ZrO₂ with Al₂O₃ will create broken atomic bonds (i.e. increase porosity) in amorphous atomic network and we believe such broken atomic bonds or nano-voids will generate additional path for transporting Li-ion through the coatings resulting less overpotential on cathode surface. Our experiments with such mixed oxides coatings indeed demonstrated improved rate performance and better capacity retention compared to uncoated and Al₂O₃-coated NMC cathodes, tested in 2032 coin-type cell assembly. Amongst the three different mixed oxide coatings Al₂O₃-ZrO₂ alloy found to be best as shown in Fig.1. An optimum mixing of Zr/(Al+Zr)=0.5 and 5-ALD cycles thickness (~0.5nm) coating helps to retain ~90% of the initial capacity of the NMC-cathode after 100 charge-discharge cycles and also it gives ~300% more improvement in capacity at 10C-rate in contrast with uncoated NMC.

To obtain nanoscale mixed-oxides coatings via ALD (as shown in Fig.2), we have demonstrated a new method ‘co-pulsing ALD’ where the metal-precursors are pulsed into reactor at the same time, instead in a sequence. That allows the metal precursors to mix into their gas-phase before reacting on cathode surface providing an intimate mixing into deposited film. The content of mixing can be controlled by varying the partial pressures of the precursors and the total thickness can be controlled by pulse number. In contrast to separate pulsing ^[6], co-pulsing allows independent control on content of mixing into ternary alloys and total thickness of the coatings at atomic-scale. That enables to tune the cathode surface properties with desirable thickness essential for the battery application.

Figure 1

Numerous studies have established that coating high voltage Li battery cathode materials with a protective film can provide enhanced cycling stability and even enhance rate performance. While the exact mechanisms by which these coatings lead to enhanced performance is still not clear, it is likely that many coatings require some ability to intercalate and diffuse Li. We have therefore investigated the Li solubility and transport properties of several high performing coating materials (bulk phases), such as Al₂O₃, AlF₃, MgO, ZrO₂, SiO₂, and related those properties to coating overpotential ¹. Furthermore, as many coatings are just a few nanometers or fewer thick, we have also the studied the influence of interfaces between very different voltage materials on their Li intercalation properties.

In our studies of coating properties and their relation to overpotential, we use ab initio density functional theory to show that the above phases, when crystalline, are very poor Li conductors except for specific directions in SiO₂. We also propose an Ohmic continuum model for understanding the connection between the coating overpotential and Li solubility, diffusivity, coating thickness, and current density. We use this model and previous studies of Li in amorphous Al₂O₃ and AlF₃ ² to predict that that Atomic Layer Deposited (ALD) Al₂O₃ coatings have a resistivity of 1789 MOhmm (10⁶Ohmm), which value is qualitatively consistent with that extracted from multiple ALD experiments (ranges from 7.8 MOhmm to 913 MOhmm). The results of our Ohmic continuum model are summarized in the attached figure, which shows our predicted coating overpotential in terms of fundamental coating properties of Li solubility and diffusivity for key systems (amorphous Al₂O₃ and AlF₃ and lithiated Li_3.5Al₂O₃) under the condition of coating thickness L=1nm, current density J _active = 0.046 mA/cm²(approximately a 1C rate) at room temperature.

In order to model the effects of interfaces on the Li energetics we explore the Li intercalation energy profile across an interface of two materials with very different bulk intercalation energies. The olivine-structured FePO₄-MPO₄ (M=Co, Ti, Mn) and layered-structured LiNiO₂-TiO₂interfaces provide model cases to understand the physics governing Li intercalation energetics across material interfaces. We find that across the interface from a high to low voltage material (i.e. low to high Li intercalation energy), the Li site energy remains constant in the high voltage material and decays approximately linearly in the low voltage region, approaching the Li site energy of the low voltage material. This effect indicates that the existence of a high intercalation voltage material at an interface can significantly enhance the Li intercalation voltage in a low voltage region over a 1-2 nm scale. We explore possible implications of this interfacial voltage enhancement for the design of novel cathode superlattice structures. 

Figure Caption:

Fig. 1 Solubility and diffusivity dependence of potential drop across a room temperature thin coating film of L = 1 nm for a current of density J_active = 0.046 mA/cm²(approximately a 1C rate) through the active coating surface.

References

1          Xu, S. et al. Lithium transport through lithium-ion battery cathode coatings. Journal of Materials Chemistry A 3, 17248-17272 (2015).

2          Hao, S. Q. & Wolverton, C. Lithium Transport in Amorphous Al2O3 and AlF3 for Discovery of Battery Coatings. J. Phys. Chem. C 117, 8009-8013, (2013).

Figure 1

To date, the preparation of free-standing 2D nanomaterials has been largely limited to the exfoliation of van der Waals solids. The lack of a robust mechanism for the bottom-up synthesis of 2D nanomaterials from non-layered materials has become an obstacle to further explore the physical properties and advanced applications of 2D nanomaterials. Here we demonstrate that surfactant monolayers can serve as soft templates guiding the nucleation and growth of 2D nanomaterials in large area beyond the limitation of van der Waals solids. One- to 2-nm-thick, single-crystalline free-standing ZnO nanosheets with sizes up to tens of micrometres are synthesized at the water–air interface. In this process, the packing density of surfactant monolayers adapts to the sub-phase metal ions and guides the epitaxial growth of nanosheets. It is thus named adaptive ionic layer epitaxy (AILE). The electronic properties of ZnO nanosheets and AILE of other materials are also investigated.

To date, the preparation of free-standing 2D nanomaterials has been largely limited to the exfoliation of van der Waals solids. The lack of a robust mechanism for the bottom-up synthesis of 2D nanomaterials from non-layered materials has become an obstacle to further explore the physical properties and advanced applications of 2D nanomaterials. Here we demonstrate that surfactant monolayers can serve as soft templates guiding the nucleation and growth of 2D nanomaterials in large area beyond the limitation of van der Waals solids. One- to 2-nm-thick, single-crystalline free-standing ZnO nanosheets with sizes up to tens of micrometres are synthesized at the water–air interface. In this process, the packing density of surfactant monolayers adapts to the sub-phase metal ions and guides the epitaxial growth of nanosheets. It is thus named adaptive ionic layer epitaxy (AILE). The electronic properties of ZnO nanosheets and AILE of other materials are also investigated.

Correction for ‘Ab initio and empirical defect modeling of LaMnO_3±δ for solid oxide fuel cell cathodes’ by Yueh-Lin Lee et al., Phys. Chem. Chem. Phys., 2012, 14, 290–302.

Correction for ‘Strain effects on oxygen migration in perovskites’ by Tam Mayeshiba et al., Phys. Chem. Chem. Phys., 2015, 17, 2715–2721.

Atomic layer deposition (ALD) of conformal AlF3 coatings onto both flat silicon substrates and high-voltage LiNi0.5Mn0.3Co0.2O2 (NMC) Li-ion battery cathode powders was investigated using a Al(CH3)3/TaF5 precursor combination. This optimized approach employs easily handled ALD precursors, while also obviating the use of highly toxic HF(g). In studies conducted on planar Si wafers, the film's growth mode was dictated by a competition between the desorption and decomposition of Ta reaction byproducts. At T ≥ 200 °C, a rapid decomposition of the Ta reaction byproducts to TaC led to continuous deposition and high concentrations of TaC in the films. A self-limited ALD growth mode was found to occur when the deposition temperature was reduced to 125 °C, and the TaF5 exposures were followed by an extended purge. The lower temperature process suppressed conversion of TaFx(CH3)5−x to nonvolatile TaC, and the long purges enabled nearly complete TaFx(CH3)5−x desorption, leaving behind the AlF3 thin films. NMC cathode powders were coated using these optimized conditions, and coin cells employing these coated cathode particles exhibited significant improvements in charge capacity fade at high discharge rates.

With the best overall electronic and thermal properties, single crystal diamond (SCD) is the extreme wide bandgap material that is expected to revolutionize power electronics and radio-frequency electronics in the future. However, turning SCD into useful semiconductors requires overcoming doping challenges, as conventional substitutional doping techniques, such as thermal diffusion and ion implantation, are not easily applicable to SCD. Here we report a simple and easily accessible doping strategy demonstrating that electrically activated, substitutional doping in SCD without inducing graphitization transition or lattice damage can be readily realized with thermal diffusion at relatively low temperatures by using heavily doped Si nanomembranes as a unique dopant carrying medium. Atomistic simulations elucidate a vacancy exchange boron doping mechanism that occurs at the bonded interface between Si and diamond. We further demonstrate selectively doped high voltage diodes and half-wave rectifier circuits using such doped SCD. Our new doping strategy has established a reachable path toward using SCDs for future high voltage power conversion systems and for other novel diamond based electronic devices. The novel doping mechanism may find its critical use in other wide bandgap semiconductors.

Perovskite oxides containing transition metals are promising materials in a wide range of electronic and electrochemical applications. However, neither their work function values nor an understanding of their work function physics have been established. Here, the work function trends of a series of perovskite (ABO₃ formula) materials using density functional theory are predicted, and show that the work functions of (001)‐terminated AO‐ and BO₂‐oriented surfaces can be described using concepts of electronic band filling, bond hybridization, and surface dipoles. The calculated range of AO (BO₂) work functions are 1.60–3.57 eV (2.99–6.87 eV). An approximately linear correlation (R² between 0.77 and 0.86 is found, depending on surface termination) between work function and position of the oxygen 2p band center, which correlation enables both understanding and rapid prediction of work function trends. Furthermore, SrVO₃ is identified as a stable, low work function, highly conductive material. Undoped (Ba‐doped) SrVO₃ has an intrinsically low AO‐terminated work function of 1.86 eV (1.07 eV). These properties make SrVO₃ a promising candidate material for a new electron emission cathode for application in high power microwave devices, and as a potential electron emissive material for thermionic energy conversion technologies.

Theoretical ORR volcano of LaBO₃perovskite (001) surfaces at stable adsorbate coverage.

This study uses first-principles methods to model Li transport, resistivity and overpotential of lithium-ion battery cathode coating materials.

We have systematically investigated the effect of local strain on electronic properties of graphene by first-principles calculations. Two major types of local strain, oriented along the zigzag and the armchair directions, have been studied. We find that local strain with a proper range and strength along the zigzag direction results in opening of significant band gaps in graphene, on the order of 10−1 eV; whereas, local strain along the armchair direction cannot open a significant band gap in graphene. Our results show that appropriate local strain can effectively open and tune the band gap in graphene; therefore, the electronic and transport properties of graphene can also be modified.

The effect of (La_0.5Sr_0.5)CoO_4±δ decoration on the time-dependent surface exchange coefficient (k^q) of epitaxial La_0.8Sr_0.2CoO_3−δ and La_0.6Sr_0.4Co_0.2Fe_0.8O_3−δ thin films.

Computational results show that a 2% biaxial tensile strain may increase oxygen ion conduction, both in- and out-of-plane, by up to approximately three orders of magnitude at 300 K in the most strain-sensitive LaBO₃ perovskites, where B = [Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Ga].

An elastoplastic phase field model is developed to investigate the role of lateral confinement on morphology of thin films grown heteroepitaxially on patterned substrates. Parameters of the model are chosen to represent InxGa1−xAs thin films growing on GaAs patterned with SiO2. We determined the effect of misfit strain on morphology of thin films grown in 0.5 μm patterns with non-uniform deposition flux. Growth of islands inside patterns can be controlled by non-uniformity of deposition flux, misfit strain between film and the substrate, and also strain energy relaxation due to plastic deformation. Our results show that the evolution of island morphology depends non-monotonically on indium content and associated misfit strain due to coupling between the plastic relaxation and the confinements effects. Low indium concentration (0%–40%) causes formation of instabilities with relatively long wavelengths across the width of the pattern. Low surface diffusion (due to low indium concentration) and fewer islands across the pattern (due to small misfit strain) lead to formation and growth of islands near the walls driven by overflow flux. Further increase in indium concentration (40%–75%) increases the lattice mismatch and surface diffusivity of the film, and also activates plastic deformation mechanism, which leads to coalescence of islands usually away from the edges. By further increasing the indium concentration (up to 100%), plastic deformation relaxes most of the strain energy density of the film, which prevents formation of instabilities in the film. Hence, in this case, islands are only formed near the walls.

The growth and properties of alloys in the alternative quaternary alloy system GaAs1−y−zPyBiz were explored. This materials system allows simultaneous and independent tuning of lattice constant and band gap energy, Eg, over a wide range for potential near- and mid-infrared optoelectronic applications by adjusting y and z in GaAs1−y−zPyBiz. Highly tensile-strained, pseudomorphic films of GaAs1−yPy with a lattice mismatch strain of ∼1.2% served as the host for the subsequent addition of Bi. Lattice-matched alloy materials to GaAs were generated by holding y ∼ 3.3z in GaAs1−y−zPyBiz. Epitaxial films with both high Bi content, z ∼ 0.0854, and a smooth morphology were realized with measured band gap energies as low as 1.11–1.01 eV, lattice-matched to GaAs substrates. Density functional theory calculations are used to provide a predictive model for the band gap of GaAs1−y−zPyBiz lattice-matched to GaAs.

Strong Sr dependence of the oxygen surface exchange kinetics for the La_2−xSr_xNiO_4±δ (LSNO) thin films.

We have investigated the migration of La³⁺ and Sr²⁺ by an A-site vacancy (V_A) mechanism in La_1-xSr_xMnO₃ (LSMO). Ab initio calculations determine migration barriers of 2.96 eV and 2.42 eV, for La³⁺ and Sr²⁺, respectively, and that repulsion between Sr²⁺ and V_A is well-described by a screened electrostatic potential of the form E = 2.8 exp(-0.2r)/r eV, (r being separation in Å). Using these results to parameterize kinetic Monte Carlo (KMC) and analytical calculations for diffusion coefficients we find good agreement with experiment observations of A-site diffusivity in other perovskites. We use these results to consider the tendency for Sr²⁺ to kinetically demix.

Complex oxides such as transition metal perovskites, particularly ABO₃ perovskites of the type ABO₃ (A=La, B= transition metal, O=oxygen) are promising catalysts for the aqueous oxygen reduction reaction (ORR), with potential application in in Alkaline Fuel Cell cathodes. There is a significant interest in using atomistic simulations to help design cathode materials through the study of the nature of surface binding. However, modeling of ORR on perovskite oxides has been previously restricted only to BO₂-terminated surfaces, although in practice a mixture of both AO and BO₂ terminations may exist, as the exact nature of surface termination for thin-film catalyst electrodes is difficult to synthesize as well as characterize. In this study we use Density Functional Theory based thermodynamic modeling to probe the differences in the binding of oxygen (O) and hydroxyl species (OH and OOH,) on the (001)-LaO and (001)-BO₂ terminations, with the focus on the material LaCrO₃. We show that in the relevant aqueous conditions for alkaline fuel cell cathodes, surface species *OH, *OOH and *O bind ~2eV more strongly to the LaO-termination compared to the CrO₂-terminated surfaces, and therefore, when the LaO termination is stable, can potentially passivate the oxygen reduction and evolution reaction activity of the thin-films of LaCrO₃.

This work demonstrates how magnetic free energy terms can be incorporated into defects models, with a focus on those used to study solid oxide fuel cell materials. We apply the approach to extending a LaMnO₃ defect model recently published in Physical Chemistry Chemical Physics 14 (1), 290-302 (2012). The magnetic contributions to the defect reactions energies are found to be much smaller than the total defect reaction energetics, and are close to the accuracy range of the adopted modeling approaches. These results show that the magnetic contributions are relatively minor and can often be ignored without significant loss of accuracy.

Metal sorption on carbon plays an important role in a range of applications, including batteries, supercapacitors, and nuclear reactors. In particular, understanding sorption of radioactive isotopes, such as Cs and Sr in different graphite materials, is important for establishing nuclear reactor safety. This work uses ab initio methods to predict the binding behavior of Cs and Sr adatoms to perfect graphite, amorphous carbon, and diamond structures. The aim of these simulations is to analyze the influence of sp2 / sp3 binding and graphite defects on adatom binding strength. Results show a tendency of defects in graphite and carbon structures with sp3 bonds to form stronger bonds with the adatoms than perfect sp2 graphite. Comparison with experimental sorption data suggests that defects and sp3 bonded regions play a critical role in the sorption behavior of Cs and Sr on graphite.

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

Anomalous small-angle X-ray scattering (ASAXS) is a powerful technique for probing catalyst particle sizes on the nanometer scale making it ideal for monitoring particle size distributions (PSDs) of Pt-based cathode electrocatalysts for polymer electrolyte fuel cells. Potential cycling experiments were performed on Pt nano-particles in both aqueous and membrane-electrode assembly (MEA) environments. PSDs were resolved through in-situ ASAXS and used to calculate geometric surface areas (GSA) and mass losses. A comparison of the losses in the GSA and mass of Pt along with changes in the evolutions of the PSDs over 900 potential cycles elucidated differences between degradation mechanisms in the two different environments. The MEA environment induced greater GSA and mass losses while maintaining similar growth in the mean particle size as compared to the aqueous environment. Results show changes in PSDs consistent with dominant mechanisms of Pt dissolution/coarsening in the aqueous environment and dissolution in the MEA environment.

We present a kinetic Monte Carlo (KMC) simulation method to study the dealloying behavior of Pt1-xCox nanoparticles. The method uses the modified embeded atom method (MEAM) to model the thermodynamics of the Pt1-xCox alloy, along with kinetic parameters fit to ab initio calculations and experimental data. We have validated the method by using ramped potential simulations. Ramped potential simulations show that dissolution occurs at the potential value expected from measured dissolution potentials.

Interaction between grain boundaries and radiation is studied in 3C-SiC by conducting molecular dynamics cascade simulations on bicrystal samples with different misorientation angles. The damage in the in-grain regions was found to be unaffected by the grain boundary type and is comparable to damage in single crystal SiC. Radiation-induced chemical disorder in the grain boundary regions is quantified using the homonuclear to heteronuclear bond ratio (χ). We found that χ increases nearly monotonically with the misorientation angle, which behavior has been attributed to the decreasing distance between the grain boundary dislocation cores with an increasing misorientation angle. The change in the chemical disorder due to irradiation was found to be independent of the type of the grain boundary.

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

The efficiency of Solid Oxide Fuel Cells depends strongly on the oxygen reduction reaction (ORR) rate on the cathode. Recent studies have suggested that large enhancements in defect thermokinetics and catalytic rates may be possible through use of epitaxially strained materials. Using ab-initio methods to simulate defect energetics under epitaxial strain, we quantify the effects of strain on oxygen stoichiometry in La{1-x}Sr{x}CoO{3-δ} (x=0.125) thin-films. We find at most a 2-3 fold increase in the vacancy concentration (at near 1000K) compared to bulk LSC over the range of strains from -2% (compressive) to about 4% (tensile). The results of this study suggest that epitaxial strain alone has a relatively weak effect of LSC oxygen vacancy concentration under SOFC conditions.

The potential use of ZrC for nuclear applications in irradiated environments makes it important to determine the structure and energetics of its point defects. In this paper the structures and energies of potential vacancy and interstitial point defects are examined by means of ab initio calculations. It is shown that C vacancies are easily formed and that their ab initio energetics are consistent with thermodynamic models of phase stability of the off-stoichiometric ZrCx (x<1) material. C interstitials are shown to be the most stable interstitial defect and form a C–C–C trimer along the ⟨101⟩ direction. C vacancies and interstitials are found to be dramatically more stable than antisite defects or Zr vacancies or interstitials.

Carbon (C)-doping has been found to increase the upper critical field HC2 in superconducting MgB2 thin-film and bulk samples. However, the C effects on both HC2 and lattice parameters are very different between thin films and bulk, suggesting C may incorporate differently in the two cases. This paper combines ab initio calculations and thin-film lattice parameter measurements to explore the connection between substitutional and interstitial C in MgB2 and experimental bulk and thin-film lattice parameters.

Loss of nanoparticle surface area under polymer electrolyte membrane fuel cell (PEMFC) operating conditions is an important barrier to the practical application of PEMFCs. We have developed an electrochemical rate theory model to better understand a number of aspects of Pt surface area loss, including the effects of particle size distribution, crossover molecular hydrogen, and Pt oxidation. As a specific example we here discuss some initial efforts to understand the effects of temperature on Pt nanoparticle stability. We compare our results to a previously developed first-order kinetic model and use this comparison to evaluate the role of the kinetic activation enthalpy for Pt dissolution and precipitation on overall surface area loss.

CsI coated C fiber cathodes are promising electron emitters utilized in field emission applications. Ab initio calculations, in conjunction with experimental investigations on CsI-spray coated C fiber cathodes, were performed in order to better understand the origin of the low turn-on E-field obtained, as compared to uncoated C fibers. One possible mechanism for lowering the turn-on E-field is surface dipole layers reducing the work function. Ab initio modeling revealed that surface monolayers of Cs, CsI, Cs2O, and CsO are all capable of producing low work function C fiber cathodes (1 eV<Φ<1.5 eV), yielding a reduction in the turn-on E-field by as much as ten times, when compared to the bare fiber. Although a CsI-containing aqueous solution is spray deposited on the C fiber surface, energy dispersive x-ray spectroscopy and scanning auger microscopy measurements show coabsorption of Cs and I into the fiber interior and Cs and O on the fiber surface, with no surface I. It is therefore proposed that a cesium oxide (CsxOy) surface coating is responsible, at least in part, for the low turn E-field and superior emission characteristics of this type of fiber cathode. This CsxOy layer could be formed during preconditioning heating. CsxOy surface layers cannot only lower the fiber work function by the formation of surface dipoles (if they are thin enough) but may also enhance surface emission through their ability to emit secondary electrons due to a process of grazing electron impact. These multiple electron emission processes may explain the reported 10–100 fold reduction in the turn-on E-field of coated C fibers.

Perovskite materials of the form ABO₃ are a promising family of compounds for use in solid oxide fuel cell (SOFC) cathodes. Study of the physics of these compounds under SOFC conditions with ab initio methods is particularly challenging due to high temperatures, exchange of oxygen with O₂ gas, and correlated electron effects. This paper discusses an approach to performing ab initio studies on these materials for SOFC applications and applies the approach to calculate vacancy formation energies in LaBO₃ (B = Mn, Fe, Co, Ni) compounds.

Scandate cathodes (BaxScyOz on W) are important thermionic electron emission materials whose emission mechanism remains unclear. Ab initio modeling is used to investigate the surface properties of both scandate and traditional B-type (Ba–O on W) cathodes. We demonstrate that the Ba–O dipole surface structure believed to be present in active B-type cathodes is not thermodynamically stable, suggesting that a nonequilibrium steady state dominates the active cathode’s surface structure. We identify a stable, low work function BaxScyOz surface structure, which may be responsible for some scandate cathode properties and demonstrate that multicomponent surface coatings can lower cathode work functions.

Recent studies have shown that high pressure ( P ) induces the metallization of the Fe ²⁺ –O bonding, the destruction of magnetic ordering in Fe, and the high-spin (HS) to low-spin (LS) transition of Fe in silicate and oxide phases at the deep planetary interiors. Hematite (Fe ₂ O ₃ ) is an important magnetic carrier mineral for deciphering planetary magnetism and a proxy for Fe in the planetary interiors. Here, we present synchrotron Mössbauer spectroscopy and X-ray diffraction combined with ab initio calculations for Fe ₂ O ₃ revealing the destruction of magnetic ordering at the hematite → Rh ₂ O ₃ -II type (RhII) transition at 70 GPa and 300 K, and then the revival of magnetic ordering at the RhII → postperovskite (PPv) transition after laser heating at 73 GPa. At the latter transition, at least half of Fe ³⁺ ions transform from LS to HS and Fe ₂ O ₃ changes from a semiconductor to a metal. This result demonstrates that some magnetic carrier minerals may experience a complex sequence of magnetic ordering changes during impact rather than a monotonic demagnetization. Also local Fe enrichment at Earth's core-mantle boundary will lead to changes in the electronic structure and spin state of Fe in silicate PPv. If the ultra-low-velocity zones are composed of Fe-enriched silicate PPv and/or the basaltic materials are accumulated at the lowermost mantle, high electrical conductivity of these regions will play an important role for the electromagnetic coupling between the mantle and the core.

The properties of (Mg,Fe)SiO₃ perovskite at lower mantle conditions are still not well understood, and particular attention has recently been given to determining the Fe spin state. A major challenge in spin states studies is interpretation of Mössbauer spectra to determine the electronic structure of iron under extreme conditions. In this paper ab initio methods are used to predict quadrupole splitting values of high‐, intermediate‐ and low‐spin Fe²⁺ and Fe³⁺ in perovskite, as a function of pressure and composition. The calculations in (Mg_0.75Fe_0.25)SiO₃ yield quadrupole splitting values in the range of 0.7–1.7 mm/s for all spin and valence states except high‐spin Fe²⁺, which has two possible quadrupole splittings, 2.3 and 3.3 mm/s. The unexpected multiple quadrupole splitting values for high‐spin Fe²⁺ are explained in terms of small changes in local structure and d‐orbital occupations. The computed results are applied to interpret existing perovskite Mössbauer data for iron's spin state.

Surface oxides on Pt play a central role in the catalytic activity and durability of Pt nanostructures, especially under potential cycling conditions. This work constructs a model for the oxide formation and removal portion of Pt cyclic voltammograms based on underlying system thermodynamics and kinetics. Limitations of the model show the need to accurately include the roles of place exchange and surface oxide phases in order to quantitatively match experimental data. However, a better understanding of surface oxidation mechanisms is still required for quantitative modeling.

Perovskite materials of the form ABO₃ are an important family of compounds for use in solid oxide fuel cell (SOFC) cathodes. In particular, their surface properties play an essential role in their catalytic ability. This paper combines bulk defect models fit to experimental data with ab initio surface energies to predict surface vacancy concentrations in (La_1-xSr_x)MnO₃. It is found that the surface vacancy concentration is ~10⁶ times that of bulk under SOFC operating conditions. The enhanced concentration of surface vacancies may play a role in O₂ dissociation, incorporation, and surface transport.

The tunneling magnetoresistance value of a Co100−xFex (4nm)∕AlOx 1.7nm∕Co100−xFex (4nm) magnetic tunnel junction has been demonstrated to depend on the composition of the Co100−xFex electrodes. The interface roughness, crystal structure, and tunneling spin polarization versus the composition of the Co100−xFex electrode were studied to address the origin of this compositional dependence. Ab initio calculations of s-like electron spin polarization predict a composition dependence similar to that observed experimentally. The combined experimental and computational results show that the trends in Co100−xFex tunneling magnetoresistance are modified slightly by the interface roughness but mainly determined by the s-like electron spin polarization values associated with different compositions and crystal structures.

Cesium-iodide (CsI)-coated graphite cathodes are promising electron sources for high power microwave generators, but the mechanism driving the improved emission is not well understood. Therefore, an ab initio modeling investigation on the effects of thin CsI coatings on graphite has been carried out. It is demonstrated that the CsI coatings reduce the work function of the system significantly through a mechanism of induced dipoles. The results suggest that work function modification is a major contribution to the improved emission seen when CsI coatings are applied to C.

We present ab initio calculations of the zero‐temperature composition dependent spin transition pressures in rocksalt (B1) (Mg_1−x,Fe_x)O. We predict that the spin transition pressure decreases with increasing Mg content, consistent with experimental results. At high‐pressure, we find that the effective size of Mg is smaller than high‐spin Fe but quite close to low‐spin Fe, consistent with a simple compression argument for how Mg reduces the spin transition pressure. We also show that the spin transition is primarily driven by the volume difference between the high‐spin and low‐spin phases, rather than changes in the electronic structure with pressure. The volume contraction at the transition is found to depend non‐monotonically on Fe content. For FeO we predict a B1 → iB8 transition at 63 GPa, consistent with previous results. However, we also predict an unexpected reverse transition of high‐spin iB8 → low‐spin B1 at approximately 400 GPa.

Two different Pt/C samples (Pt/Vulcan and Pt/C-high-surface- area) were subject to potential cycling between 0.6V and 1.0V vs. reversible hydrogen electrode, where significant platinum area loss was found. Both cycled MEA cathode samples were examined and compared in detail by glancing angle X-ray powder Diffraction and transmission electron microscopy (TEM) to reveal processes responsible for observed platinum loss. TEM data and analyses of pristine and cycled Pt/C catalyst and cross-sectional MEA cathode samples unambiguously confirmed that coarsening of platinum particles occurred via two different processes as reported recently1: i) Ostwald ripening on carbon at the nanometer- scale; and ii) diffusion of soluble platinum species in the ionomer phase at the micrometer-scale, chemical reduction of these species by cross-over H2 molecules and precipitation of platinum particles in the cathode ionomer phase.

The prediction of crystal structure is a key outstanding problem in materials science and one that is fundamental to computational materials design. We argue that by combining the predictive accuracy of quantum mechanics with data mining tools to extract knowledge from a large body of historical experimental or computational results, this problem can be successfully addressed.

Predicting crystal structure is one of the most fundamental problems in materials science and a key early step in computational materials design. Ab initio simulation methods are a powerful tool for predicting crystal structure, but are too slow to explore the extremely large space of possible structures for new alloys. Here we describe ongoing work on a novel method (Data Mining of Quantum Calculations, or DMQC) that applies data mining techniques to existing ab initio data in order to increase the efficiency of crystal structure prediction for new alloys. We find about a factor of three speedup in ab intio prediction of crystal structures using DMQC as compared to naïve random guessing. This study represents an extension of work done by Curtarolo, et al. [1] to a larger library of data.

A detailed analysis of the formation energies of transition metal hydrides is presented. The hydriding energies are computed for various crystal structures using Density Functional Theory. The process of hydride formation is broken down into three consecutive, hypothetical reactions in order to analyse the different energy contributions, and explain the observed trends. We find that the stability of the host metal is very significant in determining the formation energy, thereby providing a more fundamental justification for Miedema's “law of inverse stability” [1] (the more stable the metal, the less stable the hydride). The conversion of the host metal to the structure formed by the metal ions in the hydride (fcc in most cases) is only significant for metals with a strong bcc preference such as V and Cr - this lowers the driving force for hydride formation. The final contribution is the chemical bonding between the hydrogen and the metal. This is the only contribution that is negative, and hence favourable to hydride formation. We find that it is dominated by the position of the Fermi level in the host metal.

We study a quenching reaction occurring at sinks within a spherical cavity and at the cavity surface. One may think of reactions at these two, distinct locations as two, coupled reactive channels. Reactions of the type D*+A→D+A are studied in the limit of nondilute A, present at both locations, and dilute D, present within the cavity. We use a Monte Carlo algorithm to compute mean rates, pseudo-first-order rates and branching ratios, and compare with results obtained by assuming that the two reactive channels operate in parallel. The ratio of activities of the two channels are varied; static and moving sinks are studied. We discuss an application to the determination of pore structure by NMR (nuclear magnetic resonance).