Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Center for Advanced Systems Understanding (CASUS), Gorlitz (Germany); Helmholtz-Zentrum Dresden-Rossendorf, Dresden(Germany)
We present a numerical modeling workflow based on machine learning (ML) which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. 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.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 1817970
- Report Number(s):
- SAND2021-7532R; 699027
- Country of Publication:
- United States
- Language:
- English
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