Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy

TL;DR

This paper presents a polynomial machine learning potential tailored for the Cu-Ag-Au alloy system, enabling accurate global structure searches and property predictions, with potential applicability to other ternary alloys.

Contribution

The study introduces a novel polynomial MLP based on rotationally invariant invariants, enhancing large-scale atomistic simulations for ternary alloys.

Findings

Enables comprehensive global structure search in Cu-Ag-Au

Provides reliable property predictions across compositions

Supports efficient and accurate atomistic simulations

Abstract

Machine learning potentials (MLPs) have become indispensable for performing accurate large-scale atomistic simulations and predicting crystal structures. This study introduces the development of a polynomial MLP specifically for the ternary Cu-Ag-Au system. The MLP is formulated as a polynomial of polynomial invariants that remain unchanged under any rotation. The polynomial MLP facilitates not only comprehensive global structure searches within the Cu-Ag-Au alloy system but also reliable predictions of a wide variety of properties across the entire composition range. The developed MLP supports highly accurate and efficient atomistic simulations, thereby significantly advancing the understanding of the Cu-Ag-Au system. Furthermore, the methodology demonstrated in this study can be easily applied to other ternary alloy systems.