Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks

DOI10.1016/j.jcp.2021.110523OpenAlexW3175511888MaRDI QIDQ2132596

Lin Lin, Yixiao Chen, Linfeng Zhang, Weile Jia, Leonardo Zepeda-Núñez, Jiefu Zhang

Publication date: 28 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.00775

zbMATH Keywords

symmetryKohn-Sham density functional theorydeep neural networksself-consistent field iteration

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Numerical methods for mathematical programming, optimization and variational techniques (65Kxx) Statistical mechanics, structure of matter (82-XX)

Related Items (1)

Wide-Band Butterfly Network: Stable and Efficient Inversion Via Multi-Frequency Neural Networks

Uses Software

• Adam
• DeePMD-kit
• SchNet
• DGDFT

Cites Work

• Optimization algorithm for the generation of ONCV pseudopotentials
• Elliptic Preconditioner for Accelerating the Self-Consistent Field Iteration in Kohn--Sham Density Functional Theory
• Deep Potential: A General Representation of a Many-Body Potential Energy Surface
• Numerical methods for Kohn–Sham density functional theory
• Electronic Structure
• Iterative Procedures for Nonlinear Integral Equations

This page was built for publication: Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks