Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems-Reference-Cited by-同舟云学术

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Published:2020-06-19 Issue:8 Volume:16 Page:4757-4775
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Gkeka Paraskevi¹^ORCID,Stoltz Gabriel²³,Barati Farimani Amir⁴^ORCID,Belkacemi Zineb¹²,Ceriotti Michele⁵^ORCID,Chodera John D.⁶^ORCID,Dinner Aaron R.⁷^ORCID,Ferguson Andrew L.⁸^ORCID,Maillet Jean-Bernard⁹,Minoux Hervé¹⁰,Peter Christine¹¹^ORCID,Pietrucci Fabio¹²,Silveira Ana⁶,Tkatchenko Alexandre¹³^ORCID,Trstanova Zofia¹⁴,Wiewiora Rafal⁶,Lelièvre Tony²³

Affiliation:

1. Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France

2. CERMICS, Ecole des Ponts, Marne-la-Vallée, France

3. Matherials Project-Team, Inria Paris, 75012 Paris, France

4. Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States

5. Laboratory of Computational Science and Modelling, Institute of Materials, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

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

7. Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States

8. Pritzker School of Molecular Engineering, University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United States

9. CEA-DAM, DIF, 91297 Arpajon Cedex, France

10. Integrated Drug Discovery, Sanofi R&D, 94403 Vitry-sur-Seine, France

11. University of Konstanz, 78457 Konstanz, Germany

12. UMR CNRS 7590, MNHN, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, 75005 Paris, France

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

14. School of Mathematics, The University of Edinburgh, Edinburgh EH9 3FD, U.K.

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.0c00355

Reference231 articles.

1. Improving collective variables: The case of crystallization

2. Atom-centered symmetry functions for constructing high-dimensional neural network potentials

3. On representing chemical environments

4. Perspective: Insight into reaction coordinates and dynamics from the potential energy landscape

5. Reaction Coordinates and Mechanistic Hypothesis Tests

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