General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.

ABSTRACT: The combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT) Hamiltonian as a function of material structure, which can naturally preserve the Euclidean symmetry even in the presence of spin-orbit coupling. Our DeepH-E3 method enables efficient electronic structure calculation at ab initio accuracy by learning from DFT data of small-sized structures, making the routine study of large-scale supercells (>10⁴ atoms) feasible. The method can reach sub-meV prediction accuracy at high tr

SUBMITTER: Gong X 

PROVIDER: S-EPMC10199065 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature