Resolving Computational Challenges in Accelerating Electronic Structure Calculations using Machine Learning

Recent advances in use of machine learned surrogates to accelerate electronic structure calculations provide exciting opportunities for materials modeling. While the new models are extremely effective, the training of such models require millions of samples for predicting the material properties for a configuration of atoms or snapshot in a single temperature, atomic density pair. This results in excessively high training costs when material properties for multiple snapshots at multiple temperatures and densities are needed. We present a novel atom-centered decomposition of local density of states for supervision, which reduces the number of samples for training and evaluation by orders of magnitude compared to past approaches. Combined with a new model for learning atomic environment descriptions end-to-end, our approach allows resolving downstream quantities such as band energy of melting point aluminum at a fraction of the cost of previous state of the art, with matching or greater accuracy. We further demonstrate that the new models generalize across multiple temperatures of Aluminum reducing computational costs even further. Finally, in order to extend the approach even further we devise an uncertainty metric to choose the next snapshot for training. We demonstrate the efficacy of this metric using liquid and solid aluminum snapshots.