Machine learning of slow collective variables and enhanced sampling via spatial techniques

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 3, 2025

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

Language: Английский

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

Published: Feb. 14, 2025

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

Language: Английский

Machine Learning and Statistical Mechanics: Shared Synergies for Next Generation of Chemical Theory and Computation

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Language: Английский