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

Current Status of the MB-pol Data-Driven Many-Body Potential for Predictive Simulations of Water Across Different Phases DOI
Etienne Palos, Ethan F. Bull-Vulpe, Xuanyu Zhu

et al.

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(21), P. 9269 - 9289

Published: Oct. 14, 2024

Developing a molecular-level understanding of the properties water is central to numerous scientific and technological applications. However, accurately modeling through computer simulations has been significant challenge due complex nature hydrogen-bonding network that molecules form under different thermodynamic conditions. This complexity led over five decades research many attempts. The introduction MB-pol data-driven many-body potential energy function marked advancement toward universal molecular model capable predicting structural, thermodynamic, dynamical, spectroscopic across all phases. By integrating physics-based (i.e., machine-learned) components, which correctly capture delicate balance among interactions, achieves chemical accuracy, enabling realistic water, from gas-phase clusters liquid ice. In this review, we present comprehensive overview formalism adopted by MB-pol, highlight main results predictions made with date, discuss prospects for future extensions potentials generic reactive systems.

Language: Английский

Citations

5
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