Characterizing Metastable States with the Help of Machine Learning-Reference-Cited by-同舟云学术

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Characterizing Metastable States with the Help of Machine Learning

Published:2022-08-03 Issue:9 Volume:18 Page:5195-5202
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Novelli Pietro¹^ORCID,Bonati Luigi²^ORCID,Pontil Massimiliano¹³,Parrinello Michele²

Affiliation:

1. Computational Statistics and Machine Learning, Italian Institute of Technology, Via Enrico Melen 83, 16142 Genoa, Italy

2. Atomistic Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16142 Genoa, Italy

3. Department of Computer Science, University College London, London WC1E 6BT, United Kingdom

Funder

H2020 Industrial Leadership

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.2c00393

Reference41 articles.

1. Understanding Molecular Simulation

2. Persistent Topology and Metastable State in Conformational Dynamics

3. Robust Density-Based Clustering To Identify Metastable Conformational States of Proteins

4. Free energy disconnectivity graphs: Application to peptide models

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3. Deductive Machine Learning Challenges and Opportunities in Chemical Applications;Annual Review of Chemical and Biomolecular Engineering;2024-07-24

4. Computing the committor with the committor to study the transition state ensemble;Nature Computational Science;2024-06-05

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