Computational methods to simulate molten salt thermophysical properties

Deep Learning Potential Assisted Prediction of Local Structure and Thermophysical Properties of the SrCl2-KCl-MgCl2 Melt

The local structure and thermophysical properties of SrCl2-KCl-MgCl2 melt were revealed by deep potential molecular dynamicsdriven by machine learning to facilitate the development of molten salt electrolytic Mg-Sr alloys. The short- and intermediate-range order of the SrCl2-KCl-MgCl2 melts was explored through radial distribution functions and structure factors, respectively, and their component and temperature dependence were discussed comprehensively. In the MgCl2-rich system, the intermediate-range order is more pronounced, and its evolution with temperature exhibits a non-Debye-Waller behavior. Mg-Cl is dominated by 4,5 coordination and Sr-Cl by 6,7 coordination, and their coordination geometries exhibit distorted octahedra and distorted pentagonal bipyramids, respectively. A database of thermophysical properties of SrCl2-KCl-MgCl2 melts, including density, self-diffusion coefficient, viscosity, and ionic conductivity, was thus developed, covering the temperature range from 873 to 1173 K.

First-Principles Investigation of the Effects of UF4 and ThF4 Fuels on the Structural, Dynamic, and Thermodynamic Properties of LiF-NaF

An in-depth understanding and characterization of molten salt properties are necessary for the optimized design, efficient operation, and safety assurance of molten salt reactors (MSRs). Investigating molten salt properties in experimental settings can be challenging and time-consuming due to the high temperatures of interest, the salt's corrosiveness, purity and composition control, and health and safety concerns. Therefore, it is beneficial to perform computational screening to assist in the ultimate experimental measurements. Herein, we used first-principles molecular dynamics simulations to calculate several thermophysical, structural, and dynamic properties of eutectic LiF-NaF with fuel additives UF4 and ThF4. We found that with the incorporation of uranium or thorium, a prepeak appears in the structure factor, indicative of a medium-range structural ordering. Furthermore, we explore the mechanism through which these structural changes enhance shear stress correlations, thereby increasing the salt's viscosity. This work highlights the importance of studying the atomic-scale structure of molten salts and how the addition of fuel elements can substantially affect it.

Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability.

TrIP─Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias

Accurate interatomic energies and forces enable high-quality molecular dynamics simulations, torsion scans, potential energy surface mappings, and geometry optimizations. Machine learning algorithms have enabled rapid estimates of the energies and forces with high accuracy. Further development of machine learning algorithms holds promise for producing universal potentials that support many different atomic species. We present the Transformer Interatomic Potential (TrIP): a chemically sound potential based on the SE(3)-Transformer. TrIP's species-agnostic architecture, which uses continuous atomic representation and homogeneous graph convolutions, encourages parameter sharing between atomic species for more general representations of chemical environments, maintains a reasonable number of parameters, serves as a form of regularization, and is a step toward accurate universal interatomic potentials. TrIP achieves state-of-the-art accuracies on the COMP6 benchmark with an energy prediction of just 1.02 kcal/mol MAE. We introduce physical bias in the form of Ziegler-Biersack-Littmark-screened nuclear repulsion and constrained atomization energies. An energy scan of a water molecule demonstrates that these changes improve long- and near-range interactions compared to other neural network potentials. TrIP also demonstrates stability in molecular dynamics simulations, demonstrating reasonable exploration of Ramachandran space.