TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations-Reference-Cited by-同舟云学术

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations

Published:2024-05-14 Issue:10 Volume:20 Page:4076-4087
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Pelaez Raul P.¹^ORCID,Simeon Guillem¹^ORCID,Galvelis Raimondas¹²^ORCID,Mirarchi Antonio¹^ORCID,Eastman Peter³^ORCID,Doerr Stefan²^ORCID,Thölke Philipp²^ORCID,Markland Thomas E.³^ORCID,De Fabritiis Gianni¹²⁴^ORCID

Affiliation:

1. Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain

2. Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain

3. Department of Chemistry, Stanford University, Stanford, California 94305, United States

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

Funder

Ministerio de Ciencia e Innovaci?n

Ag?ncia de Gesti? d'Ajuts Universitaris i de Recerca

Horizon 2020 Framework Programme

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00253

Reference69 articles.

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2. Kocer, E., Ko, T. W., Behler, J. Neural Network Potentials: A Concise Overview of Methods. 2021, arXiv. 10.48550/arXiv.2107.03727.

3. Perspective: Machine learning potentials for atomistic simulations

4. SchNet – A deep learning architecture for molecules and materials

5. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

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