Numerical Modeling of Kohn-Sham Density Functional Theory with Deep Neural Networks

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

We present a numerical modeling workflow based on machine learning (ml) which reproduces the 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 total free energy, which serves as the born-oppenheimer potential energy surface for the atoms.We demonstrate the effectiveness of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum.