Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations

Abstract

We propose a new scheme based on machine learning for the efficient screening in grain-boundary (GB) engineering. A set of results obtained from first-principles calculations based on density functional theory (DFT) for a small number of GB systems is used as a training data set. In our scheme, by partitioning the total energy into atomic energies using a local-energy analysis scheme, we can increase the training data set significantly. We use atomic radial distribution functions and additional structural features as atom descriptors to predict atomic energies and GB energies simultaneously using the least absolute shrinkage and selection operator, which is a recent standard regression technique in statistical machine learning. In the test study with fcc-Al [110] symmetric tilt GBs, we could achieve enough predictive accuracy to understand energy changes at and near GBs at a glance, even if we collected training data from only 10 GB systems. The present scheme can emulate time-consuming DFT calculations for large GB systems with negligible computational costs, and thus enable the fast screening of possible alternative GB systems.