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Machine-learned prediction of the electronic fields in deformed crystals

SwarnavaGhosh's picture

Dear Colleagues,

I am writing to share an article titled, "Machine-learned prediction of the electronic fields in a crystal", co-authored by Ying-Shi Teh, Professor Kaushik Bhattacharya and myself. The article is published in the journal Mechanics of Materials. Link to the article:                               

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.




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