Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation.

ABSTRACT: The marriage of density functional theory (DFT) and deep-learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent the DFT Hamiltonian (DeepH) of crystalline materials, aiming to bypass the computationally demanding self-consistent field iterations of DFT and substantially improve the efficiency of ab initio electronic-structure calculations. A general framework is proposed to deal with the large dimensionality and gauge (or rotation) covariance of the DFT Hamiltonian matrix by virtue of locality, and this is realized by a message-passing neural network for deep learning. High accuracy, high efficiency and good transferability of the DeepH method are generally demonstrated for various kinds of mat

SUBMITTER: Li H 

PROVIDER: S-EPMC11499279 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature