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Published December 2021 | Submitted
Journal Article Open

Machine-learned prediction of the electronic fields in a crystal


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.

Additional Information

© 2021 Published by Elsevier Ltd. Received 16 April 2021, Revised 17 July 2021, Accepted 14 September 2021, Available online 25 September 2021. We are grateful to the De Logi foundation and the Army Research Laboratory, USA (under Cooperative Agreement Number W911NF-12-2-0022) for their generous support of the research. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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August 22, 2023
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