Learning the committor without collective variables

doi:10.1038/s43588-026-00958-2

Learning the committor without collective variables

2026-02-17

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Sergio Contreras Arredondo

DOI：10.1038/s43588-026-00958-2

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Here we introduce a graph neural network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables. The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the importance of each heavy atom in the transition mechanism. It also yields precise estimates of the rate constants for the underlying processes. The proposed approach assists in understanding and modeling complex dynamics, by enabling collective-variable-free learning and automated identification of physically meaningful reaction coordinates of complex molecular processes. By learning directly from atomic motion, without the need for handcrafted descriptors, a graph neural network reveals how molecular systems change state, delivering accurate kinetics and atom-level insight into complex transitions.

Keywords:

Biological physics

Statistical mechanics

Computer Science

general

Learning the committor without collective variables

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