Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling

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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Cited by in corpus (12)

• A unified framework for machine learning collective variables for enhanced sampling simulations: $\text{mlcolvar}$
• Deep Learning Collective Variables from Transition Path Ensemble
• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
• Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling
• Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
• Spectral Map: Embedding Slow Kinetics in Collective Variables
• Manifold Learning in Atomistic Simulations: A Conceptual Review
• Deep learning the slow modes for rare events sampling
• Selecting High-Dimensional Representations of Physical Systems by Reweighted Diffusion Maps
• Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles
• Learning Markovian Dynamics with Spectral Maps
• Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics