Repository: Freie Universität Berlin, Math Department

Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning

Noé, F. and Olsson, S. and Köhler, J. and Wu, H. (2019) Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning. Science, 365 (6457). eaaw1147.


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Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples directly, vast computational effort is invested for simulating these system in small steps, e.g., using Molecular Dynamics. Combining deep learning and statistical mechanics, we here develop Boltzmann Generators, that are shown to generate statistically independent samples of equilibrium states of representative condensed matter systems and complex polymers. Boltzmann Generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free energy differences, and discovery of new system states are demonstrated, providing a new statistical mechanics tool that performs orders of magnitude faster than standard simulation methods.

Item Type:Article
Additional Information:SFB1114 Preprint 12/2018 in arXiv:1812.01729 (
Subjects:Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Mathematical and Computer Sciences > Artificial Intelligence > Machine Learning
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Molecular Biology
ID Code:2295
Deposited By: Silvia Hoemke
Deposited On:07 Feb 2019 12:54
Last Modified:21 Oct 2019 15:09

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