Prediction of grain boundary structure and energy by machine learning

TL;DR

This paper introduces a machine learning approach to efficiently predict grain boundary structures and energies, significantly accelerating materials research by reducing computational costs.

Contribution

The study develops a machine learning method trained on copper grain boundaries to accurately predict structures and energies of various grain boundaries, enhancing understanding of complex interfaces.

Findings

Accurately predicts grain boundary structures and energies

Reduces computational effort in studying grain boundaries

Demonstrates general applicability to complex interfaces

Abstract

Grain boundaries dramatically affect the properties of polycrystalline materials because of differences in atomic configuration. To fully understand the relationship between grain boundaries and materials properties, systematic studies of the grain boundary atomic structure are crucial. However, such studies are limited by the extensive computation necessary to determine the structure of a single grain boundary. If the structure could be predicted with more efficient computation, the understanding of the grain boundary would be accelerated significantly. Here, we predict grain boundary structures and energies using a machine-learning technique. Training data for non-linear regression of four symmetric-tilt grain boundaries of copper were used. The results of the regression analysis were used to predict 12 other grain boundary structures. The method accurately predicts both the structures and energies of grain boundaries. The method presented in this study is very general and can be utilized in understanding many complex interfaces.