Atomistic methods are the standard workhorse for calculating many properties, including grain boundary energy (GBE). GBE is notoriously difficult to measure experimentally, so atomistic calculations are often used as the de facto benchmark for GBE calculations. But are they reliable?
In this paper, we investigated GBE reports from 225 different materials across 5 species, using the OpenKIM framework. For aluminum alone, grain boundary simulations were performed for 58 different potentials. While all potentials reported remarkable consistency regarding the location and relative magnitudes of energy minima (“cusps”), surprisingly, a broad spread of energy values were reported spanning nearly an order of magnitude. Which potential is correct? It is difficult to say.
Link to paper: https://doi.org/10.1016/j.commatsci.2023.112057
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.