Neural-network Density Functional Theory Based on Variational Energy Minimization-Reference-Cited by-同舟云学术

Neural-network Density Functional Theory Based on Variational Energy Minimization

Published:2024-08-12 Issue:7 Volume:133 Page:
ISSN:0031-9007
Container-title:Physical Review Letters
language:en
Short-container-title:Phys. Rev. Lett.

Author:

Li Yang¹^ORCID,Tang Zechen¹^ORCID,Chen Zezhou¹^ORCID,Sun Minghui¹^ORCID,Zhao Boheng¹^ORCID,Li He¹¹^ORCID,Tao Honggeng¹,Yuan Zilong¹,Duan Wenhui¹¹²^ORCID,Xu Yong¹²³^ORCID

Affiliation:

1. Tsinghua University

2. Frontier Science Center for Quantum Information

3. RIKEN Center for Emergent Matter Science (CEMS)

Funder

National Natural Science Foundation of China

National Science Fund for Distinguished Young Scholars

Beijing Innovation Center for Future Chip

National Key Research and Development Program of China

Beijing Advanced Innovation Center for Materials Genome Engineering

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevLett.133.076401/fulltext

Reference29 articles.

1. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

2. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

3. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

4. Neural network representation of electronic structure from ab initio molecular dynamics

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1. Neural-network Density Functional Theory Based on Variational Energy Minimization;Physical Review Letters;2024-08-12