A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics

We present a graph-based differentiable representation learning method from atomic coordinates for enhanced sampling methods to learn both thermodynamic and kinetic properties of a system.

Authors

Ziyue Zou
Pratyush Tiwary (ORCID: https://orcid.org/0000-0002-2412-6922)
Dedi Wang (ORCID: https://orcid.org/0000-0002-7524-5809)

Institutions

Foundation for the National Institutes of Health (US)
University of Maryland, College Park (US)

Publication Details

Journal: Digital Discovery
Published: 2024-11-28
DOI: https://doi.org/10.1039/d4dd00315b
Citations: 12
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 1.41

Funders

Basic Energy Sciences

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article

A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics

Ziyue Zou, Pratyush Tiwary, Dedi Wang

Nov 28, 2024

12 citations

Digital Discovery

Machine Learning in Materials Science

1.41

article

A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics

Ziyue Zou, Pratyush Tiwary, Dedi Wang

November 28, 2024article en

12 citations

Abstract

We present a graph-based differentiable representation learning method from atomic coordinates for enhanced sampling methods to learn both thermodynamic and kinetic properties of a system.

Digital DiscoveryVol. 4(1)

Foundation for the National Institutes of Health (US), University of Maryland, College Park (US)

Basic Energy Sciences

Openalex Percentile: Top 15%

Machine Learning in Materials Science

1.41

DOI Open Access PDF OpenAlex

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A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics — Ziyue Zou, Pratyush Tiwary, et al. · Digital Discovery (2024) | TGRS Research Map | TGRS