Nonlinear Discovery of Slow Molecular Modes using Hierarchical Dynamics Encoders

by   Wei Chen, et al.
The University of Chicago

The success of enhanced sampling molecular simulations that accelerate along collective variables (CVs) is predicated on the availability of variables coincident with the slow collective motions governing the long-time conformational dynamics of a system. It is challenging to intuit these slow CVs for all but the simplest molecular systems, and their data-driven discovery directly from molecular simulation trajectories has been a central focus of the molecular simulation community to both unveil the important physical mechanisms and to drive enhanced sampling. In this work, we introduce hierarchical dynamics encoder (HDE) as a deep learning architecture that learns nonlinear CV approximants to the leading slow eigenfunctions of the spectral decomposition of the transfer operator that evolves equilibrium-scaled probability distributions through time. Orthogonality of the learned CVs is naturally imposed within network training without added regularization. The CVs are inherently explicit and differentiable functions of the input coordinates making them well-suited to use in enhanced sampling calculations. We demonstrate the utility of HDEs in capturing parsimonious nonlinear representations of complex system dynamics in applications to 1D and 2D toy systems where the true eigenfunctions are exactly calculable and to molecular dynamics simulations of alanine dipeptide and the WW domain protein.


page 6

page 9

page 10

page 11

page 12

page 18


Transferable neural networks for enhanced sampling of protein dynamics

Variational auto-encoder frameworks have demonstrated success in reducin...

A microscopic description of acid-base equilibrium

Acid-base reactions are ubiquitous in nature. Understanding their mechan...

Automated design of collective variables using supervised machine learning

Selection of appropriate collective variables for enhancing sampling of ...

Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations

Markov state models (MSMs) and Master equation models are popular approa...

Please sign up or login with your details

Forgot password? Click here to reset