Application of machine learning potentials to predict grain boundary properties in fcc elemental metals

TL;DR

This paper demonstrates that machine learning potentials can accurately predict grain boundary energies in face-centered-cubic metals, matching DFT results without including boundary structures in training.

Contribution

It introduces the application of machine learning potentials to predict grain boundary properties in FCC metals, showing high accuracy without training on boundary structures.

Findings

MLPs accurately predict grain boundary energies

Predictions agree with DFT calculations

No boundary structures included in training data

Abstract

Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculation. In this study, we apply machine learning potentials in a recently constructed repository to the prediction of the grain boundary energy in face-centered-cubic elemental metals, i.e., Ag, Al, Au, Cu, Pd, and Pt. The systematic application of machine learning potentials shows that they enable us to predict grain boundary structures and their energies accurately. The grain boundary energies predicted by the MLPs are in agreement with those calculated by DFT, although no grain boundary structures were included in training datasets of the present MLPs.