Combining transition path sampling with data-driven collective variables through a reactivity-biased shooting algorithm
arxiv(2024)
摘要
Rare event sampling is a central problem in modern computational chemistry
research. Among the existing methods, transition path sampling (TPS) can
generate unbiased representations of reaction processes. However, its
efficiency depends on the ability to generate reactive trial paths, which in
turn depends on the quality of the shooting algorithm used. We propose a new
algorithm based on the shooting success rate, i.e. reactivity, measured as a
function of a reduced set of collective variables (CVs). These variables are
extracted with a machine learning approach directly from TPS simulations, using
a multi-task objective function. Iteratively, this workflow significantly
improves shooting efficiency without any prior knowledge of the process. In
addition, the optimized CVs can be used with biased enhanced sampling
methodologies to accurately reconstruct the free energy profiles. We tested the
method on three different systems: a two-dimensional toy model, conformational
transitions of alanine dipeptide, and hydrolysis of acetyl chloride in bulk
water. In the latter, we integrated our workflow with an active learning scheme
to learn a reactive machine learning-based potential, which allowed us to study
the mechanism and free energy profile with an ab initio-like accuracy.
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