most citedDeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer

5 citations · 5 across the 2 of their papers we have counts for

collaborators

2 papers

cond-mat.mtrl-sci2024

Equivariant Neural Network Force Fields for Magnetic Materials

Zilong Yuan, Zhiming Xu, He Li +6

Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is stil…

physics.comp-ph2024★ 5 cited

DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer

Yuxiang Wang, He Li, Zechen Tang +6

Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architec…