ArticleThe journal of physical chemistry. B2025
Using Time Dependent Rate Analysis to Evaluate the Quality of Machine Learned Reaction Coordinates for Biasing and Computing Kinetics.
Article in The journal of physical chemistry. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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- Stepping Up Enhanced Rate Calculations with EATR-Flooding.Journal of chemical theory and computation · 2026Article
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Abstract
Having an accurate reaction coordinate (RC) is essential for reliable kinetic characterization of molecular processes, but there are few quantitative metrics to evaluate RC quality. In this study, we consider the dimensionless γ metric from the Exponential Average Time-dependent Rate (EATR) method, which represents the fraction of a biasing potential along the RC that contributes to increasing the rate constant. We demonstrate that γ can be used to test whether the utility of a RC for predicting kinetics with a Metadynamics bias improves as the coordinate is iteratively updated to include new data. We evaluate RCs approximated via the iterative State Predictive Information Bottleneck (SPIB) approach, which was previously shown to be accurate across six protein-ligand dissociation systems. For these same systems, we compute γ values and mean accelerated times τ̅
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