Higher-order equivariant neural networks for charge density prediction in materials  

作　　者：Teddy Koker Keegan Quigley Eric Taw Kevin Tibbetts Lin Li 

机构地区：[1]MIT Lincoln Laboratory,Lexington,MA,USA [2]Material Science Division,Lawrence Berkeley National Laboratory,Berkeley,CA,USA

出　　处：《npj Computational Materials》2024年第1期1566-1575,共10页计算材料学(英文)

基　　金：supported by the Under Secretary of Defense for Research and Engineeringunder Air Force Contract No.FA8702-15-D-0001.

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.

关 键 词：EQUIVARIANT CHARGE enable 

分 类 号：O17[理学—数学]