Machine learning determination of atomic dynamics at grain boundaries

TL;DR

This paper uses machine learning to link local atomic structure to dynamics in grain boundaries of polycrystalline materials, revealing predictable rearrangement behaviors and energetic characteristics.

Contribution

It introduces a structural quantity called softness to predict atomic rearrangements in grain boundaries, extending previous glassy material models to polycrystalline systems.

Findings

Softness accurately predicts atomic rearrangements.

Atomic rearrangement probability follows an Arrhenius form.

Entropy variations influence atomic dynamics significantly.

Abstract

In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity, softness, that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements, while finding a large variability within high-energy grain boundaries. As has been found in glasses [9,19,26], the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.