Finding dominant transition pathways via global optimization of action

TL;DR

This paper introduces Action-CSA, a global optimization method for sampling multiple reaction pathways efficiently, accurately capturing dominant and alternative pathways without prior pathway guesses.

Contribution

The paper presents a novel computational approach, Action-CSA, that samples multiple reaction pathways via global optimization of the Onsager-Machlup action using conformational space annealing.

Findings

Successfully samples multiple pathways for alanine dipeptide and hexane

Accurately reproduces transition time distributions compared to molecular dynamics

Identifies folding pathways consistent with experiments and simulations

Abstract

We present a new computational approach, Action-CSA, to sample multiple reaction pathways with fixed initial and final states through global optimization of the Onsager-Machlup action using the conformational space annealing method. This approach successfully samples not only the most dominant pathway but also many other possible paths without initial guesses on reaction pathways. Pathway space is efficiently sampled by crossover operations of a set of paths and preserving the diversity of sampled pathways. The sampling ability of the approach is assessed by finding pathways for the conformational changes of alanine dipeptide and hexane. The benchmarks demonstrate that the rank order and the transition time distribution of multiple pathways identified by the new approach are in good agreement with those of long molecular dynamics simulations. We also show that the lowest action folding pathway of the mini-protein FSD-1 identified by the new approach is consistent with previous molecular dynamics simulations and experiments.