Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments-Reference-Cited by-同舟云学术

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments

Published:2024-04-05 Issue:14 Volume:10 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Unke Oliver T.¹²³^ORCID,Stöhr Martin⁴^ORCID,Ganscha Stefan¹^ORCID,Unterthiner Thomas¹^ORCID,Maennel Hartmut¹^ORCID,Kashubin Sergii¹^ORCID,Ahlin Daniel¹^ORCID,Gastegger Michael²³⁵^ORCID,Medrano Sandonas Leonardo⁴^ORCID,Berryman Joshua T.⁴^ORCID,Tkatchenko Alexandre⁴^ORCID,Müller Klaus-Robert¹²⁶⁷⁸^ORCID

Affiliation:

1. Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland.

2. Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany.

3. DFG Cluster of Excellence “Unifying Systems in Catalysis” (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany.

4. Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.

5. BASLEARN — TU Berlin/BASF Joint Lab for Machine Learning, Technische Universität Berlin, 10587 Berlin, Germany.

6. Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea.

7. Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany.

8. BIFOLD — Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.

Abstract

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

Publisher

American Association for the Advancement of Science (AAAS)

Link

https://www.science.org/doi/pdf/10.1126/sciadv.adn4397

Reference150 articles.

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