Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques
This work addresses a fundamental problem in chemical physics for researchers using atomistic simulations, but it is incremental as it reviews existing spatial techniques without presenting new results.
The paper tackles the challenge of identifying collective variables (CVs) for understanding long-time dynamics in complex physical processes, focusing on techniques that use spatial data instead of temporal trajectories to find slow CVs, and reviews recent developments and potential future directions in this area.
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.