Calculation update! New properties have been added to the website for dislocation monopole core structures, dynamic relaxes of both crystal and liquid phases, and melting temperatures! Currently, the results for these properties predominately focus on EAM-style potentials, but the results will be updated for other potentials as the associated calculations finish. Feel free to give us feedback on the new properties so we can improve their representations as needed.
Warning! Note that elemental potentials taken from alloy descriptions may not work well for the pure species. This is particularly true if the elements were fit for compounds instead of being optimized separately. As with all interatomic potentials, please check to make sure that the performance is adequate for your problem.
Citation: L. Guo, Y. Liu, L. Yang, and B. Cao (2025), "Lattice dynamics modeling of thermal transport in solids using machine-learned atomic cluster expansion potentials: A tutorial", Journal of Applied Physics137(8). DOI: 10.1063/5.0251119.
Abstract: Lattice dynamics (LD) plays a crucial role in investigating thermal transport in terms of not only underlying physics but also novel properties and phenomena. Recently, machine learning interatomic potentials (MLIPs) have emerged as powerful tools in computational physics and chemistry, showing great potential in providing reliable predictions of thermal transport properties with high efficiency. This tutorial provides a comprehensive guideline for MLIPs' development and how they are used for the computational modeling of thermal transport. Using atomic cluster expansion (ACE) as the paradigmatic potential, we introduce the essential fundamentals of MLIPs, including data construction, model training, and hyperparameter optimization. With the developed ACE potentials, we further showcase their applications in the LD modeling of thermal transport for crystalline silicon and amorphous carbon. The corresponding code implementations for MLIP applications in calculating thermal conductivity are also provided for beginners to follow.
See Computed Properties Notes: This file was provided by Sergei Starikov on July 15, 2026. The links were pulled from the paper and contain training and testing data and example fitting workflow code. File(s):