Linearized machine learning potential with high-order rotational polynomial invariants for multi-component systems

Machine-learning potential (MLP) providing an accurate description of the structure-energy relationship and its potential applications are of growing interest. Such an approach is based on a linearized MLP framework, which was successful in constructing accurate MLPs in a variety of elemental metals [1]. Also, the introduction of group-theoretical high-order rotational polynomial invariants can contribute to systematically derive MLPs with high predictive power for a wide range of structures, including extreme structures [2]. The present study proposes a formulation of linearized MLP extended to multi-component systems involving high-order rotational invariants. We also show its applications to several binary alloy and ionic systems such as Ti-Al system. For each system, we obtain MLPs with high predictive power and Pareto frontier MLPs for the computational cost versus the accuracy.

[1] A. Takahashi, A. Seko, and I. Tanaka, J. Chem. Phys. 148, 234106 (2018).
[2] A. Seko, A. Togo, and I. Tanaka, Phys. Rev. B 99, 214108 (2019).