The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small Σ (Σ<10) GBs of more than 50 metals, we develop a model that can predict the grain boundary energies to within a mean absolute error of 0.13 J m−2. More importantly, this universal GB energy model can be extrapolated to the energies of high Σ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for materials science.
@article{arxiv.2201.11991,
title = {A Universal Machine Learning Model for Elemental Grain Boundary Energies},
author = {Weike Ye and Hui Zheng and Chi Chen and Shyue Ping Ong},
journal= {arXiv preprint arXiv:2201.11991},
year = {2022}
}