2023/12/21 by Arthur France‐Lanord, Hadrien Vroylandt, France-Lanord, Arthur +9 · 4 citations
Materials Science · Physics and Astronomy · #Chemical Physics (physics.chem-ph) #Complex Network Analysis Techniques #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Theoretical and Computational Physics
paper · pdf · doi:10.48550/arxiv.2312.13868
openalex publication_date 2023/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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, which can be thought of as a data-driven generalization of the path collective variable concept. It consists in 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 to reach 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.