Data-Driven Path Collective Variables

doi:10.1021/acs.jctc.4c00123

Data-Driven Path Collective Variables

2024-04-15

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Arthur France‐Lanord *

DOI：10.1021/acs.jctc.4c00123

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Identifying optimal collective variables to model transformations using atomic-scale simulations is a long-standing challenge. We propose a new method for the generation, optimization, and comparison of collective variables that can be thought of as a data-driven generalization of the path collective variable concept. It consists of a kernel ridge regression of the committor probability, which encodes a transformation's progress. The resulting collective variable is one-dimensional, interpretable, and differentiable, making it appropriate for enhanced sampling simulations requiring biasing. We demonstrate the validity of the method on two different applications: a precipitation model and the association of Li+ and F- in water. For the former, we show that global descriptors such as the permutation invariant vector allow reaching an accuracy far from the one achieved via simpler, more intuitive variables. For the latter, we show that information correlated with the transformation mechanism is contained in the first solvation shell only and that inertial effects prevent the derivation of optimal collective variables from the atomic positions only.

Keywords:

ION-PAIR DISSOCIATION

TRANSITION-STATES

DIFFUSION MAPS

OPTIMIZATION

PARAMETERS

SEPARATION

DYNAMICS

ENERGY

Data-Driven Path Collective Variables

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