Quantifying Energetic and Entropic Pathways in Molecular Systems

TL;DR

This paper demonstrates the effectiveness of the State Predictive Information Bottleneck (SPIB) method in identifying energetic and entropic pathways in molecular systems, enhancing understanding of transition mechanisms at non-zero temperatures.

Contribution

The paper introduces and validates SPIB as a novel machine learning approach for discovering non-linear reaction coordinates that capture both energy and entropy effects in complex molecular dynamics.

Findings

SPIB accurately predicts entropic bottlenecks in analytical systems.

SPIB identifies energy- and entropy-dominated pathways in multi-well systems.

SPIB uncovers entropic and energetic barriers in benzoic acid permeation simulations.

Abstract

When examining dynamics occurring at non-zero temperatures, both energy and entropy must be taken into account while describing activated barrier crossing events. Furthermore, good reaction coordinates need to be constructed to describe different metastable states and the transition mechanisms between them. Here we use a physics-based machine learning method called the State Predictive Information Bottleneck (SPIB) to find non-linear reaction coordinates for three systems of varying complexity. The SPIB is able to predict correctly an entropic bottleneck for an analytical flat-energy double-well system and identify the entropy- and energy-dominated pathways for an analytical four-well system. Finally, for a simulation of benzoic acid permeation through a lipid bilayer, SPIB is able to discover the entropic and energetic barriers to the permeation process. Given these results, we thus establish that SPIB is a reasonable and robust method for finding the important entropy and energy/enthalpy barriers in physical systems, which can then be used for enhanced understanding and sampling of different activated mechanisms.