Information Entropy is a General-Purpose Collective Variable for Enhanced Sampling

TL;DR

This paper introduces a novel information entropy-based collective variable for enhanced sampling, enabling unbiased discovery of transition pathways in diverse molecular systems without prior knowledge.

Contribution

The authors propose a general-purpose entropy-based CV that facilitates unsupervised exploration of complex energy landscapes in atomistic simulations.

Findings

Successfully applied to five different systems including nucleation and phase transformations.

Enables discovery of metastable states and transition pathways inaccessible to traditional CVs.

Balances exploration and thermodynamic relevance through a well-tempered metadynamics approach.

Abstract

Enhanced sampling methods typically require predefined collective variables (CVs) that presuppose knowledge of reaction coordinates, restricting the discovery of unanticipated transition mechanisms or intermediates. Here, we show that a local measure of information entropy in atomistic systems is a general-purpose CV for rare event sampling across molecular and condensed-phase systems. The method biases simulations toward entropy-changing configurations following a well-tempered metadynamics approach, thus balancing novelty and thermodynamic accessibility. Blind exploration of potential energy surfaces enables unsupervised discovery of metastable basins and reaction pathways, including competing transition channels inaccessible to conventional order parameters. We demonstrate the generality of the method across five systems spanning conformational sampling, homogeneous nucleation, glass formation, and solid-state phase transformations.