Stephen Lam

Silicon is the dominant solar material because of its abundance, low cost, and high solar efficiency. But manufacturing high-purity silicon required for solar energy is very complex, hard to scale, and unsafe since it involves dealing with toxic flammable gases. Therefore, a new solar silicon production technology based on molten salt electrolysis has been proposed. To comprehensively study the self-diffusion and deposition mechanism of silicon ions in high-temperature molten salt electrolyte and their composition and temperature dependence, both interatomic potential molecular dynamics (IPMD) and ab-initio molecular dynamics (AIMD) are introduced. Thermodynamics and transport properties of molten salt electrolyte such as density, heat capacity, viscosity etc. are investigated and compared to the existed experimental results. Also, local structural information and coordination number analysis are obtained and discussed.

Molten salts are a promising class of ionic liquids used in advanced energy applications including next-generation nuclear reactors, batteries, and solar thermal energy storage. In these applications, understanding corrosion processes and predicting phase behavior remains a critical challenge. This requires accurate prediction of the solvation thermodynamics of ionic species in a variety of chemical and configurational states. In this work, we fundamentally address these challenges by combining quasichemical theory (QCT), ab initio simulation with density functional theory (DFT), and neural network interatomic potentials (NNIP) to accurately predict the solvation free energy of solute ions in molten salt. Ab initio data is used to train neural networks that learn the environment-dependent atomic forces and energies. This enables acceleration of atomistic simulation by more than three orders of magnitude. Using chemically accurate and highly efficient neural network-based molecular simulations, we perform free energy calculations within the QCT framework. Namely, QCT provides an exact partitioning of the free energy that includes contributions from 1) formation of a cavity in solution, 2) insertion of a solute ion into the cavity, and 3) relaxation of the cavity surrounding the solute ion. This requires simulations in timescales totaling tens of nanoseconds. As such, using AIMD alone is impractical for exploring a wide range of solutes, compositions, and thermodynamic conditions. In this work, we show that the NNIPs can accurately predict molten salt thermodynamics and local coordination structures. We provide a demonstration of the combined methods (DFT-NNIP-QCT) on molten NaCl, in which we obtain the total excess potentials of Na⁺ and Cl^- ions, and perform corrections to errors in electrostatic energy caused by finite size of the simulation cell. The calculated excess chemical potential for Na+/Cl− was predicted to be -161.7±10.6 kcal/mol, which is consistent with previous calculations and an experimental value of -163.5 kcal/mol from thermochemical tables. These results provide initial validation of the methods for predicting excess chemical potentials, which can be directly exploited for the determination of solute chemistry, and the solubility of dissolved gases and metallic ions in molten salts. This provides motivation for the use of these methods to understanding solute chemistry in a wide range of molten salt systems in advanced energy applications.

LiF–NaF–ZrF₄ multicomponent molten salts are identified as promising candidates for coolant salts in molten salt reactors and advanced high-temperature reactors. This study focused on low-melting point salt compositions of interest: 38LiF–51NaF–11ZrF₄, 42LiF–29NaF–29ZrF₄, and 26LiF–37NaF–37ZrF₄. Ab-initio molecular dynamics (AIMD) calculations were performed and compared with available experimental data to assess the ability of rigid ion models (RIM) to reproduce short- to intermediate-range structure, transport, and thermophysical properties of the LiF–NaF–ZrF₄ salt mixtures. It is found that as ZrF₄ mol% increases, the average cation–anion coordination number (CN) of monovalent cations (Li⁺, Na⁺) obtained from RIM calculations decreases, while multivalent Zr⁴⁺ CN varied from 15% to 19% in comparison to corresponding AIMD values. In addition, RIM is found to predict the existence of 7, 8, and 9 coordinated fluorozirconate complexes, while AIMD and the available experimental data showed an occurrence of 6, 7, and 8 coordinated complexes in the melt. The intermediate-range structure analysis revealed that while the RIM parameters are able to reproduce a local structure for lower ZrF₄ mol% salts such as in 38LiF–51NaF–11ZrF₄, an extensive fluorozirconate network formation is observed in RIM simulations for higher ZrF₄ mol% compositions. The network generated by RIM parameters is found to be mainly connected by “corner-sharing” fluorozirconate complexes as opposed to both “edge-sharing” and “corner-sharing” connectively portrayed by AIMD. It is found that a close agreement between AIMD and the RIM salt structure for the 11-mol% ZrF₄ salt resulted in good agreement in the calculated Zr diffusivities and the viscosity values. However, due to the inaccurate short- to intermediate-range structure prediction by RIM for higher ZrF₄ mol% compositions, thermophysical properties such as densities and heat capacity differ by up to 26% and 27%, respectively, upon comparison with AIMD and experimental values. Also, the network-dominated properties such as diffusion coefficients and viscosities differed by up to two and three orders of magnitude, respectively. This study signifies the importance of accurate salt structure generation for an accurate prediction of transport and thermophysical properties of multicomponent molten salts.

Solvation thermodynamics in molten salt is accurately and efficiently predicted by combining ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and quasichemical theory (QCT).

Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3¾F₂ (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of desirable heat-transfer and neutron-absorption characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIP) provide a fast and accurate method for performing molecular dynamics of molten salts. For LiF, these potentials are able to accurately model dimer interactions, crystalline solids under deformation, semi-crystalline LiF near the melting point and liquid LiF at high temperatures. For Flibe, NNIPs accurately predicts the structures and dynamics at normal operating conditions, high temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long timescales (e.g., nanosecond) and large system sizes (e.g., 10⁵ atoms), while maintaining ab initio accuracy and providing more than three orders of magnitude of computational speedup for calculating structure and transport properties.

In molten fluoride salt systems, the chemistry and transport of hydrogen are coupled to its valence state, which controls the balance of tritium leakage and corrosion.

The high temperature gas-cooled reactor pebble-bed module (HTR-PM) in China received much attention for its inherent safety performance and high thermal efficiency. The generation mechanism, distribution, reduction route, and release type of tritium (H-3) in HTR-PM are presented with a complete theoretical model. The calculation results indicate the majority of H-3 in the core is generated by the activation reaction of B-10. The activity concentration of H-3 in the primary loop and the specific activity of H-3 in the secondary loop at the operating equilibrium are computed as 3.69 × 10⁶ Bq/m3STPof helium and 4.22 × 10⁴ Bq/kg of water, respectively. The H-3 sampling measurement in HTR-PM has been designed to collect data from the primary coolant, from the liquid waste storage tank, from the secondary coolant, and from the liquid and gaseous effluents, separately. In this paper, the design of H-3 sampling positions in the helium purification system is discussed. The H-3 sampling measurement from the primary helium in HTR-PM has been improved, which can provide reliable activity concentration data of H-3 in the primary loop and supply accurate evaluation for the efficiency of the helium purification system.