ArticleQRB discovery2023
Chasing collective variables using temporal data-driven strategies.
Article in QRB discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Learning the reaction coordinate: collective variables from physical intuition to generative models.Digital discovery · 2026Review
- Adaptive Tensor Train Metadynamics for High-Dimensional Free Energy Exploration.Journal of chemical theory and computation · 2026Article
- Thermodynamic and Kinetic Analysis of Molecular Conformational Dynamics in a Riemannian Framework.The journal of physical chemistry. A · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Calculating Free-Energy Differences Using an Average Force: A Tutorial for Adaptive Biasing Force Simulations.The journal of physical chemistry. B · 2025Article
- Iterative variational learning of committor-consistent transition pathways using artificial neural networks.Nature computational science · 2025Article
- Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles.Journal of chemical theory and computation · 2024Article
- tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation.Journal of chemical theory and computation · 2023Article
- Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning.Journal of chemical theory and computation · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The convergence of free-energy calculations based on importance sampling depends heavily on the choice of collective variables (CVs), which in principle, should include the slow degrees of freedom of the biological processes to be investigated. Autoencoders (AEs), as emerging data-driven dimension reduction tools, have been utilised for discovering CVs. AEs, however, are often treated as black boxes, and what AEs actually encode during training, and whether the latent variables from encoders are suitable as CVs for further free-energy calculations remains unknown. In this contribution, we review AEs and their time-series-based variants, including time-lagged AEs (TAEs) and modified TAEs, as well as the closely related model variational approach for Markov processes networks (VAMPnets). We then show through numerical examples that AEs learn the high-variance modes instead of the slow modes. In stark contrast, time series-based models are able to capture the slow modes. Moreover, both modified TAEs with extensions from slow feature analysis and the state-free reversible VAMPnets (SRVs) can yield orthogonal multidimensional CVs. As an illustration, we employ SRVs to discover the CVs of the isomerizations of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.