Deep learning density functional theory and beyond

First-principles methods based on density functional theory (DFT) have become indispensable tools in physics, chemistry, materials science, etc., but are bottlenecked by the efficiency-accuracy dilemma. The integration of first-principles methods with deep learning offers a transformative opportunity to overcome these limitations. In this talk, I will explore the emerging interdisciplinary field of deep-learning DFT, which employs advanced deep learning techniques to address key limitations in DFT computations. Specifically, I will present our recent work on developing a deep neural network framework, DeepH, that learns the relationship between the DFT Hamiltonian and atomic structures [1-3]. Trained on DFT data for small structures, these neural network models can generalize to predict properties of unseen large material structures without invoking time-consuming DFT self-consistent field iterations, making efficient and accurate study of large-scale materials feasible. Combined with recent methodological developments, these innovations pave the way for deep-learning electronic structure calculations [4-12]. This paradigm shift promises to transform the landscape of first-principles computations, significantly accelerating future materials discovery and design.

References:

[1] H. Li, et al. Nature Computational Science 2, 367 (2022) arXiv: 2104.03786