Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks-Reference-Cited by-同舟云学术

Toward High-level Machine Learning Potential for Water Based on Quantum Fragmentation and Neural Networks

Published:2022-06-09 Issue:24 Volume:126 Page:3926-3936
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Liu Jinfeng¹²^ORCID,Lan Jinggang³,He Xiao²⁴^ORCID

Affiliation:

1. Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China

2. Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China

3. Chaire de Simulation à l’Echelle Atomique (CSEA), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

4. New York University-East China Normal University Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China

Funder

Ministry of Science and Technology of the People's Republic of China

Central University Basic Research Fund of China

National Natural Science Foundation of China

Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, East China Normal University

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.2c00601

Reference127 articles.

1. Liquid water contains the building blocks of diverse ice phases

2. How Water’s Properties Are Encoded in Its Molecular Structure and Energies

3. Relationship between structural order and the anomalies of liquid water

4. Advances in Computational Studies of the Liquid–Liquid Transition in Water and Water-Like Models

5. Metastable liquid–liquid transition in a molecular model of water

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