Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power

TL;DR

This study evaluates the accuracy and limitations of linearized pairwise and angular-dependent machine-learning interatomic potentials across 31 elemental metals, highlighting the importance of descriptors for predictive power.

Contribution

It demonstrates that a unified linearized framework can produce accurate MLIPs for various metals and clarifies the role of pairwise versus angular descriptors in different metal types.

Findings

Linearized MLIPs are robust and accurate for all 31 metals.

Pairwise descriptors suffice for most non-transition metals.

Angular descriptors are crucial for transition metals.

Abstract

Machine-learning interatomic potential (MLIP) has been of growing interest as a useful method to describe the energetics of systems of interest. In the present study, we examine the accuracy of linearized pairwise MLIPs and angular-dependent MLIPs for 31 elemental metals. Using all of the optimal MLIPs for 31 elemental metals, we show the robustness of the linearized frameworks, the general trend of the predictive power of MLIPs and the limitation of pairwise MLIPs. As a result, we obtain accurate MLIPs for all 31 elements using the same linearized framework. This indicates that the use of numerous descriptors is the most important practical feature for constructing MLIPs with high accuracy. An accurate MLIP can be constructed using only pairwise descriptors for most non-transition metals, whereas it is very important to consider angular-dependent descriptors when expressing interatomic interactions of transition metals.