In this work, we developed a compositionally transferable machine learning interatomic potential using atomic cluster expansion potential and PBE-D3 method for (NaCl)1-x(MgCl2)x molten salt and we showed that it is possible to fit a robust potential for this pseudo-binary system by only including data from x={0, 1/3, 2/3, 1}. We also assessed the performance of several DFT methods including PBE-D3, PBE-D4, R2SCAN-D4, and R2SCAN-rVV10 on unary NaCl and MgCl2 salts. Our results show that the R2SCAN-D4 method calculates the thermophysical properties of NaCl and MgCl2 with an overall modestly better accuracy compared to the other three methods.
Partitioning interatomic force constants for first-principles phonon calculations: Applications to NaCl, PbTiO$_3$, monolayer CrI$_3$, and twisted bilayer graphene
Machine-Learned Interatomic Potentials for Predicting Physicochemical Properties of Molten Metal-Salt Systems for Calcium Electrolysis
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Digital Materials LLC（数字材料有限责任公司）；Institute of High Temperature Electrochemistry, Ural Branch of the Russian Academy of Sciences（俄罗斯科学院乌拉尔分院高温电化学研究所）
Machine-learning octet $AB$-type binary compounds across chemical space with domain knowledge of the interatomic bond
Ruhr-Universität Bochum（波鸿鲁尔大学）；Federal Institute for Materials Research and Testing（联邦材料研究与测试研究所）；Friedrich-Schiller-Universität Jena（耶拿弗里德里希·席勒大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
From Energy-Force Weighting to Primal-Dual Optimization of Machine-Learned Interatomic Potentials
School of Mathematical Sciences, Institute of Natural Sciences and MOE-LSC, Shanghai Jiao Tong University（上海交通大学数学科学学院、自然科学研究院和教育部线性科学中心）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）
