Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

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).