Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces

Elia Schneider, Luke Dai, Robert Q. Topper, Christof Drechsel-Grau, Mark E. Tuckerman

Research output: Contribution to journalArticlepeer-review


The generation of free energy landscapes corresponding to conformational equilibria in complex molecular systems remains a significant computational challenge. Adding to this challenge is the need to represent, store, and manipulate the often high-dimensional surfaces that result from rare-event sampling approaches employed to compute them. In this Letter, we propose the use of artificial neural networks as a solution to these issues. Using specific examples, we discuss network training using enhanced-sampling methods and the use of the networks in the calculation of ensemble averages.

Original languageEnglish (US)
Article number150601
JournalPhysical Review Letters
Issue number15
StatePublished - Oct 11 2017

ASJC Scopus subject areas

  • General Physics and Astronomy


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