Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
This enables multiscale materials modeling under extreme conditions at previously unattainable computational scales, though it appears incremental as it applies existing neural network methods to accelerate a specific computational bottleneck.
The researchers tackled the computational expense of finite-temperature Kohn-Sham density functional theory (DFT) by developing a machine learning workflow using deep neural networks that reproduces DFT total energies within chemical accuracy at negligible cost, demonstrated for solid and liquid aluminum.
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 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.