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. 2017 Oct 13;119(15):150601.
doi: 10.1103/PhysRevLett.119.150601. Epub 2017 Oct 11.

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

Elia Schneider  1 , Luke Dai  1 , Robert Q Topper  2 , Christof Drechsel-Grau  1 , Mark E Tuckerman  1   3   4
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Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces

Elia Schneider et al. Phys Rev Lett. .

Abstract

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.

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