摘要
The calculation of electron density distribution using density functional theory(DFT)in materials and molecules is central to the study of their quantum and macro-scale properties,yet accurate and efficient calculation remains a long-standing challenge.Weintroduce ChargE3Net,an E(3)-equivariant graph neural network for predicting electron density in atomic systems.ChargE3Net enables the learning of higher-order equivariant features to achieve high predictive accuracy and model expressivity.
基金
supported by the Under Secretary of Defense for Research and Engineeringunder Air Force Contract No.FA8702-15-D-0001.
