Electronic Structure Prediction Of Multi-million Atom Systems Through Uncertainty Quantification Enabled Transfer Learning

This article discusses the use of machine learning models to predict the ground state electron density in materials simulations. By leveraging transfer learning and comprehensive sampling of system configurations, the models are able to make accurate predictions for a wide variety of systems with lower data generation costs and uncertainty quantification.