Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation-Reference-Cited by-同舟云学术

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Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation

Published:2024-07-29 Issue:15 Volume:20 Page:6559-6568
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Yang Soojung¹^ORCID,Nam Juno²^ORCID,Dietschreit Johannes C. B.²³^ORCID,Gómez-Bombarelli Rafael²^ORCID

Affiliation:

1. Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

2. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

3. Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria

Funder

Takeda Science Foundation

Massachusetts Institute of Technology

Deutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der Wissenschaften

Ilju Overseas PhD Fellowship

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00435

Reference46 articles.

1. Free Energy Calculations

2. Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]

3. Statistical mechanics of isomerization dynamics in liquids and the transition state approximation

4. TRANSITION PATH SAMPLING: Throwing Ropes Over Rough Mountain Passes, in the Dark

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