Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
ORAL
Abstract
We present a numerical modeling workflow based on deep neural networks that reproduce spatially-resolved, energy-resolved, and integrated quantities of Kohn-Sham density functional theory at finite electronic temperature to within chemical accuracy. We demonstrate the efficacy of this approach for both solid and liquid metals. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.
[1] J. A. Ellis, A. Cangi, N. Modine, J. A. Stephens, A. P. Thompson, and S. Rajamanickam, arXiv:2010.04905 (2020).
[1] J. A. Ellis, A. Cangi, N. Modine, J. A. Stephens, A. P. Thompson, and S. Rajamanickam, arXiv:2010.04905 (2020).
*AC acknowledges funding from the Center for Advanced Systems Understanding which is financed by the German Federal Ministry of Education and Research (BMBF) and by the Saxon State Ministry for Science, Art, and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly-owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
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Presenters
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Attila Cangi
- CASUS, Helmholtz Zentrum Dresden-Rossendorf
- Center for Advanced Systems Understanding (CASUS)
- Helmholtz Zentrum Dresden-Rossendorf
- Center for Advanced Systems Understanding (CASUS), Helmholtz Zentrum Dresden-Rossendorf