Enhanced sampling of transition states

Abstract

The free energy landscapes of several fundamental processes are characterized by high barriers separating long-lived metastable states. In order to explore these type of landscapes enhanced sampling methods are used. While many such methods are able to obtain sufficient sampling in order to draw the free energy, the transition states are often sparsely sampled. We propose an approach based on the Variationally Enhanced Sampling Method to enhance sampling in the transition region. To this effect, we introduce a dynamic target distribution which uses the derivative of the instantaneous free energy surface to locate the transition regions on the fly and modulate the probability of sampling different regions. Finally, we exemplify the effectiveness of this approach in enriching the number of configurations in the transition state region in the cases of a chemical reaction and of a nucleation process.

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Cited by in corpus (9)

• Rethinking Metadynamics: from bias potentials to probability distributions
• Enhanced sampling methods for molecular dynamics simulations
• The Exploration of Chemical Reaction Networks
• A Unified Approach to Enhanced Sampling
• Deep Learning Collective Variables from Transition Path Ensemble
• Gaussian Mixture Based Enhanced Sampling
• Kinetics of aqueous media reactions via ab initio enhanced molecular dynamics: the case of urea decomposition
• Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations
• A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes