Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques

TL;DR

This paper reviews recent spatial machine learning techniques for identifying slow collective variables in complex systems, enabling enhanced sampling without relying on temporal data, thus improving understanding of long-time dynamics.

Contribution

It introduces and discusses spatial learning methods for slow CVs that do not require temporal trajectories, highlighting recent advances and future directions in the field.

Findings

Spatial techniques can identify slow CVs without temporal data.

Recent methods improve sampling efficiency in complex systems.

Potential for thermodynamics-informed spatial learning is discussed.

Abstract

Understanding the long-time dynamics of complex physical processes depends on our ability to recognize patterns. To simplify the description of these processes, we often introduce a set of reaction coordinates, customarily referred to as collective variables (CVs). The quality of these CVs heavily impacts our comprehension of the dynamics, often influencing the estimates of thermodynamics and kinetics from atomistic simulations. Consequently, identifying CVs poses a fundamental challenge in chemical physics. Recently, significant progress was made by leveraging the predictive ability of unsupervised machine learning techniques to determine CVs. Many of these techniques require temporal information to learn slow CVs that correspond to the long timescale behavior of the studied process. Here, however, we specifically focus on techniques that can identify CVs corresponding to the slowest transitions between states without needing temporal trajectories as input, instead using the spatial characteristics of the data. We discuss the latest developments in this category of techniques and briefly discuss potential directions for thermodynamics-informed spatial learning of slow CVs.