Deep learning-based prediction of self-energies from ab initio dynamical mean-field theory for real materials with minimal data sets

Abstract

Density-functional theory (DFT) has been the workhorse of first-principles calculations for decades, and DFT-derived energies and forces are now widely used to train machine learning models of inter-atomic potentials. However, DFT’s single-particle treatment of exchange-correlation functionals severely limits accuracy for materials with open d- and f-shell elements, and ML models trained on such data inherit this limitation. Dynamical mean-field theory (DMFT) addresses this limitation by explicitly incorporating local electronic correlations, albeit at a significantly higher computational cost. In this work, we develop deep-learning models trained on ab-initio DFT+DMFT calculations to predict electronic self-energies from non-interacting Green’s functions. Using the correlated metal SrVO3 as a prototype, we show that accurate self-energy predictions can be achieved from small datasets. Through transfer-learning, models pre-trained on SrVO3 successfully predict the self-energies of CaVO3, BaVO3 and SrNbO3, despite differences in composition and electronic structure. Moreover, models pretrained on SrVO3 and SrNbO3 can predict self-energy of BaNbO3 without training on its self-energy. This approach captures temperature variation, extends beyond d1 perovskites and drastically reduces computational time. These results establish deep-learning as an efficient surrogate for computationally demanding DMFT calculations, enabling rapid prediction of correlation-driven properties, paving the way for a transformative shift in materials theory.