Collective Variables for Free Energy Surface Tailoring -- Understanding and Modifying Functionality in Systems Dominated by Rare Events

TL;DR

This paper presents a machine learning approach using Harmonic Linear Discriminant Analysis to identify and modify collective variables, enabling targeted alteration of free energy surfaces and system functionality in rare-event dominated systems.

Contribution

The method automatically constructs interpretable collective variables from short simulations, allowing precise modification of free energy landscapes without extensive prior knowledge.

Findings

Successfully tailored free energy surfaces in three systems

Altered thermodynamic and kinetic properties effectively

Minimal prior knowledge required for modifications

Abstract

We introduce a method for elucidating and modifying the functionality of systems dominated by rare events that relies on the automated tuning of their underlying free energy surface. The proposed approach seeks to construct collective variables (CVs) that encode the essential information regarding the rare events of the system of interest. The appropriate CVs are identified using Harmonic Linear Discriminant Analysis (HLDA), a machine-learning based method that is trained solely on data collected from short ordinary simulations in the relevant metastable states of the system. Utilizing the interpretable form of the resulting CVs, the critical interaction potentials that determine the system's rare transitions are identified and purposely modified to tailor the free energy surface in manner that alters functionality as desired. The applicability of the method is illustrated in the context of three different systems, thereby demonstrating that thermodynamic and kinetic properties can be tractably modified with little to no prior knowledge or intuition.