Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects-Reference-Cited by-同舟云学术

Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects

Published:2023 Issue:20 Volume:14 Page:5438-5452
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Jaffrelot Inizan Théo¹,Plé Thomas¹^ORCID,Adjoua Olivier¹,Ren Pengyu²,Gökcan Hatice³^ORCID,Isayev Olexandr³^ORCID,Lagardère Louis¹⁴^ORCID,Piquemal Jean-Philip¹²^ORCID

Affiliation:

1. Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris, 75005, France

2. Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA

3. Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

4. Sorbonne Université, Institut Parisien de Chimie Physique et Théorique, FR 2622 CNRS, Paris, France

Abstract

Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models.

Funder

H2020 European Research Council

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/SC/D2SC04815A

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