Noé, F. and Wu, H. (2018) Boltzmann Generators  Sampling Equilibrium States of ManyBody Systems with Deep Learning. SFB 1114 Preprint in arXiv:1812.01729 . pp. 117. (Unpublished)

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Official URL: https://arxiv.org/abs/1812.01729
Abstract
Computing equilibrium states in condensedmatter manybody systems, such as solvated proteins, is a longstanding 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 

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:  08 Feb 2019 16:13 
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