Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design

Wei Chen, Aik Rui Tan, Andrew L. Ferguson

Research output: Contribution to journalArticlepeer-review

Abstract

Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable expressions for the nonlinear collective variables, making it ideally suited for integration with enhanced sampling techniques for accelerated exploration of configurational space. In this work, we describe a number of sophistications of the neural network architectures to improve and generalize the process of interleaved collective variable discovery and enhanced sampling. We employ circular network nodes to accommodate periodicities in the collective variables, hierarchical network architectures to rank-order the collective variables, and generalized encoder-decoder architectures to support bespoke error functions for network training to incorporate prior knowledge. We demonstrate our approach in blind collective variable discovery and enhanced sampling of the configurational free energy landscapes of alanine dipeptide and Trp-cage using an open-source plugin developed for the OpenMM molecular simulation package.

Original languageEnglish (US)
Article number072312
JournalJournal of Chemical Physics
Volume149
Issue number7
DOIs
StatePublished - Aug 21 2018

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

Fingerprint Dive into the research topics of 'Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design'. Together they form a unique fingerprint.

Cite this