Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles DOI Creative Commons
Jakub Rydzewski

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

Understanding the behavior of complex molecular systems is a fundamental problem in physical chemistry. To describe long-time dynamics such systems, which responsible for their most informative characteristics, we can identify few slow collective variables (CVs) while treating remaining fast as thermal noise. This enables us to simplify and treat it diffusion free-energy landscape spanned by CVs, effectively rendering Markovian. Our recent statistical learning technique, spectral map [Rydzewski, J.

Language: Английский

Machine learning of slow collective variables and enhanced sampling via spatial techniques DOI Open Access
Tuğçe Gökdemir, Jakub Rydzewski

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

Citations

0
Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques DOI Creative Commons
Tuğçe Gökdemir, Jakub Rydzewski

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

Citations

0
Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles DOI Creative Commons
Jakub Rydzewski

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

Understanding the behavior of complex molecular systems is a fundamental problem in physical chemistry. To describe long-time dynamics such systems, which responsible for their most informative characteristics, we can identify few slow collective variables (CVs) while treating remaining fast as thermal noise. This enables us to simplify and treat it diffusion free-energy landscape spanned by CVs, effectively rendering Markovian. Our recent statistical learning technique, spectral map [Rydzewski, J.

Language: Английский

Citations

3