Automated determination of grain boundary energy and potential-dependence using the OpenKIM framework

TL;DR

This paper introduces a systematic, open-source framework within OpenKIM for calculating grain boundary energies across various materials and interatomic potentials, enabling automated, comparable, and extendable analysis.

Contribution

It develops a methodology for automated grain boundary energy calculation using OpenKIM, validated against a geometric model, and provides a platform for ongoing, standardized predictions.

Findings

GB energy predictions correlate with the geometric model.

IP choice influences energy levels but not qualitative trends.

The spread in predictions offers a measure of uncertainty.

Abstract

We present a systematic methodology, built within the Open Knowledgebase of Interatomic Models (OpenKIM) framework (https://openkim.org), for quantifying properties of grain boundaries (GBs) for arbitrary interatomic potentials (IPs), GB character, and lattice structure and species. The framework currently generates results for symmetric tilt GBs in cubic materials, but can be readily extended to other types of boundaries. In this paper, GB energy data are presented that were generated automatically for Al, Ni, Cu, Fe, and Mo with 225 IPs; the system is installed on openkim.org and will continue to generate results for all new IPs uploaded to OpenKIM. The results from the atomistic calculations are compared to the lattice matching model, which is a semi-analytic geometric model for approximating GB energy. It is determined that the energy predicted by all IPs (that are stable for the given boundary type) correlate closely with the energy from the model, up to a multiplicative factor. It thus is concluded that the qualitative form of the GB energy versus tilt angle is dominated more by geometry than the choice of IP, but that the IP can strongly affect the energy level. The spread in GB energy predictions across the ensemble of IPs in OpenKIM provides a measure of uncertainty for GB energy predictions by classical IPs.