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Machine-learned prediction of the electronic fields in a crystal

Teh, Ying Shi and Ghosh, Swarnava and Bhattacharya, Kaushik (2021) Machine-learned prediction of the electronic fields in a crystal. . (Unpublished)

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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.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Teh, Ying Shi0000-0003-1743-4158
Ghosh, Swarnava0000-0003-3800-5264
Bhattacharya, Kaushik0000-0003-2908-5469
Additional Information:Attribution 4.0 International (CC BY 4.0). We are grateful to the De Logi foundation and the Army Research Laboratory (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.
Funding AgencyGrant Number
De Logi FoundationUNSPECIFIED
Army Research LaboratoryW911NF-12-2-0022
Record Number:CaltechAUTHORS:20210412-100652607
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108693
Deposited By: Tony Diaz
Deposited On:12 Apr 2021 17:16
Last Modified:12 Apr 2021 17:16

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