Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations. In this work, we proposed a general analytic Hamiltonian representation in an E(3) equivariant framework, which can fit the ab initio Hamiltonian of molecules and solids by a complete data-driven method and are equivariant under rotation, space inversion, and time reversal operations. Our model reached state-of-the-art precision in the benchmark test and accurately predicted the electronic Hamiltonian matrices and related properties of various periodic and aperiodic systems, showing high transferability and generalization ability. This framework provides a general transferable model that can be used to accelerate the electronic structure calculations on different large systems with the same network weights trained on small structures.
@article{arxiv.2210.16190,
title = {Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids},
author = {Yang Zhong and Hongyu Yu and Mao Su and Xingao Gong and Hongjun Xiang},
journal= {arXiv preprint arXiv:2210.16190},
year = {2023}
}