NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics-Reference-Cited by-同舟云学术

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics

Published:2023-09-11 Issue:18 Volume:63 Page:5701-5708
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Galvelis Raimondas¹²^ORCID,Varela-Rial Alejandro³^ORCID,Doerr Stefan³^ORCID,Fino Roberto¹^ORCID,Eastman Peter⁴^ORCID,Markland Thomas E.⁴^ORCID,Chodera John D.⁵^ORCID,De Fabritiis Gianni²⁶³^ORCID

Affiliation:

1. Acellera Labs, C/Doctor Trueta 183, Barcelona 08005, Spain

2. Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Doctor Aiguader 88, Barcelona 08003, Spain

3. Acellera Ltd, Devonshire House 582 Honeypot Lane, Stanmore Middlesex, HA7 1JS, United Kingdom

4. Department of Chemistry, Stanford University, 337 Campus Drive, Stanford, California 94305, United States

5. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States

6. Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain

Funder

Agencia Estatal de Investigaci?n

Ministerio de Ciencia e Innovaci?n

Departament d'Empresa i Coneixement, Generalitat de Catalunya

Chan Zuckerberg Initiative

Horizon 2020 Framework Programme

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00773

Reference71 articles.

1. Performance of the Cell processor for biomolecular simulations

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