Computing the Committor with the Committor: an Anatomy of the Transition State Ensemble

Abstract

Determining the kinetic bottlenecks that make transitions between metastable states difficult is key to understanding important physical problems like crystallization, chemical reactions, or protein folding. In all these phenomena, the system spends a considerable amount of time in one metastable state before making a rare but important transition to a new state. The rarity of these events makes their direct simulation challenging, if not impossible. We propose a method to explore the distribution of configurations that the system passes as it translocates from one metastable basin to another. We shall refer to this set of configurations as the transition state ensemble. We base our method on the committor function and the variational principle to which it obeys. We find the minimum of the variational principle via a self-consistent procedure that does not require any input besides the knowledge of the initial and final state. Right from the start, our procedure focuses on sampling the transition state ensemble and allows harnessing a large number of such configurations. With the help of the variational principle, we perform a detailed analysis of the transition state ensemble, ranking quantitatively the degrees of freedom mostly involved in the transition and opening the way for a systematic approach for the interpretation of simulation results and the construction of collective variables.

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Enhanced sampling methods for molecular dynamics simulations
• A Variational Approach to Enhanced Sampling and Free Energy Calculations
• A unified framework for machine learning collective variables for enhanced sampling simulations: $\text{mlcolvar}$
• Deep Learning Collective Variables from Transition Path Ensemble
• Characterizing metastable states with the help of machine learning

Cited by in corpus (9)

• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
• Descriptors-free Collective Variables From Geometric Graph Neural Networks
• Everything everywhere all at once: a probability-based enhanced sampling approach to rare events
• Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
• Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles
• Flow Matching for Optimal Reaction Coordinates of Biomolecular System
• Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics
• Understanding Reaction Mechanisms from Start to Finish
• Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates