Arthur France-Lanord, Hadrien Vroylandt, Mathieu Salanne, Benjamin Rotenberg, A Marco Saitta, Fabio Pietrucci
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...
April 15, 2024: Journal of Chemical Theory and Computation