Machine-learned prediction of the electronic fields in a crystal

TL;DR

This paper introduces a machine learning approach to accurately predict electronic fields in strained crystalline solids, reducing the need for costly density functional theory calculations in materials design and defect analysis.

Contribution

It presents a novel machine learning framework trained on DFT data to efficiently approximate electronic properties in crystals under deformation.

Findings

Achieves chemical accuracy in energy and electronic field predictions.

Successfully captures lattice instabilities in magnesium.

Reduces computational cost for materials simulations.

Abstract

We propose an approach for exploiting machine learning to approximate electronic fields in crystalline solids subjected to deformation. Strain engineering is emerging as a widely used method for tuning the properties of materials, and this requires repeated density functional theory calculations of the unit cell subjected to strain. Repeated unit cell calculations are also required for multi-resolution studies of defects in crystalline solids. We propose an approach that uses data from such calculations to train a carefully architected machine learning approximation. We demonstrate the approach on magnesium, a promising light-weight structural material: we show that we can predict the energy and electronic fields to the level of chemical accuracy, and even capture lattice instabilities.