ArticleThe journal of physical chemistry. A2021
Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling.
Article in The journal of physical chemistry. A, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks.Journal of chemical information and modeling · 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
- Enhanced Sampling with Machine Learning.Annual review of physical chemistry · 2024Review
- Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics.The journal of physical chemistry. B · 2024Article
- Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning.Journal of chemical theory and computation · 2023Article
- Spectral Map: Embedding Slow Kinetics in Collective Variables.The journal of physical chemistry letters · 2023Article
- Selecting High-Dimensional Representations of Physical Systems by Reweighted Diffusion Maps.The journal of physical chemistry letters · 2023Article
- Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations.Journal of chemical theory and computation · 2022Article
- Enhancing the Inhomogeneous Photodynamics of Canonical Bacteriophytochrome.The journal of physical chemistry. B · 2022Article
- Deep learning the slow modes for rare events sampling.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling.The journal of physical chemistry. A · 2021Article
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2 authors.
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Abstract
Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few generalized degrees of freedom, referred to as collective variables (CVs), to represent and drive the sampling of the free energy landscape. In theory, these CVs should separate different metastable states and correspond to the slow degrees of freedom of the studied physical process. To this aim, we propose a new method that we call multiscale reweighted stochastic embedding (MRSE). Our work builds upon a parametric version of stochastic neighbor embedding. The technique automatically learns CVs that map a high-dimensional feature space to a low-dimensional latent space via a deep neural network. We introduce several new advancements to stochastic neighbor embedding methods that make MRSE especially suitable for enhanced sampling simulations: (1) weight-tempered random sampling as a landmark selection scheme to obtain training data sets that strike a balance between equilibrium representation and capturing important metastable states lying higher in free energy; (2) a multiscale representation of the high-dimensional feature space via a Gaussian mixture probability model; and (3) a reweighting procedure to account for training data from a biased probability distribution. We show that MRSE constructs low-dimensional CVs that can correctly characterize the different metastable states in three model systems: the Müller-Brown potential, alanine dipeptide, and alanine tetrapeptide.
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