Committors without Descriptors
Peilin Kang, Jintu Zhang, Enrico Trizio, TingJun Hou, and Michele Parrinello

TL;DR
This paper introduces a novel, automated committor-based sampling method that leverages graph neural networks to directly process atomic coordinates, improving the study of rare events in atomistic simulations.
Contribution
It combines a variational, neural-network-based committor approach with graph neural networks to enhance sampling of rare events without relying on predefined descriptors.
Findings
Effective in benchmark systems
Improves sampling of transition states
Captures solvent effects explicitly
Abstract
The study of rare events is one of the major challenges in atomistic simulations, and several enhanced sampling methods towards its solution have been proposed. Recently, it has been suggested that the use of the committor, which provides a precise formal description of rare events, could be of use in this context. We have recently followed up on this suggestion and proposed a committor-based method that promotes frequent transitions between the metastable states of the system and allows extensive sampling of the process transition state ensemble. One of the strengths of our approach is being self-consistent and semi-automatic, exploiting a variational criterion to iteratively optimize a neural-network-based parametrization of the committor, which uses a set of physical descriptors as input. Here, we further automate this procedure by combining our previous method with the expressive…
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Taxonomy
TopicsMachine Learning in Materials Science · Computational Drug Discovery Methods · Advanced Graph Neural Networks
