Spectral Map: Embedding Slow Kinetics in Collective Variables

Abstract

The dynamics of physical systems that require high-dimensional representation can often be captured in a few meaningful degrees of freedom called collective variables (CVs). However, identifying CVs is challenging and constitutes a fundamental problem in physical chemistry. This problem is even more pronounced when CVs information about slow kinetics related to rare transitions between long-lived metastable states. To address this issue, we propose an unsupervised deep-learning method called spectral map. Our method constructs slow CVs by maximizing the spectral gap between slow and fast eigenvalues of a transition matrix estimated by an anisotropic diffusion kernel. We demonstrate our method in several high-dimensional reversible folding processes.

Published version

References in corpus (2)

• Unsupervised machine learning in atomistic simulations, between predictions and understanding
• Selecting High-Dimensional Representations of Physical Systems by Reweighted Diffusion Maps

Cited by in corpus (1)

• Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles