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AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular Simulations
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    AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular Simulations
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    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2020, 16, 7, 4685–4693
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    https://doi.org/10.1021/acs.jctc.0c00205
    Published June 15, 2020
    Copyright © 2020 American Chemical Society

    Abstract

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    Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of the conformational space. Over several decades, many approaches have been used to overcome the problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, we recast adaptive sampling schemes on the basis of multi-armed bandits and develop a novel adaptive sampling algorithm under this framework, AdaptiveBandit. We test it on multiple simplified potentials and in a protein folding scenario. We find that this framework performs similarly to or better than previous methods in every type of test potential. Furthermore, it provides a novel framework to develop new sampling algorithms with better asymptotic characteristics.

    Copyright © 2020 American Chemical Society

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    Cited By

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    This article is cited by 25 publications.

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    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2020, 16, 7, 4685–4693
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.0c00205
    Published June 15, 2020
    Copyright © 2020 American Chemical Society

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