Ab initio calculation of real solids via neural network ansatz

• Published in: Nature communications Vol. 13; no. 1; pp. 7895 - 9
• Main Authors: Li, Xiang, Li, Zhe, Chen, Ji
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/766/119; 639/766/94; Boundary conditions; Condensed matter physics; Electron gas; Electronic structure; Graphene; Humanities and Social Sciences; Learning algorithms; Lithium; Lithium hydrides; Machine learning; Materials science; Molecular modelling; multidisciplinary; Neural networks; Physics; Science; Science (multidisciplinary); Solids; Wave functions

Neural networks have been applied to tackle many-body electron correlations for small molecules and physical models in recent years. Here we propose an architecture that extends molecular neural networks with the inclusion of periodic boundary conditions to enable ab initio calculation of real solid...