Molecular simulations are widely applied in the study of chemical and bio-physical problems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper, we propose a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their crosstalks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations through deep generative learning; in turn, FG simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method defines a variational and adaptive training objective, which allows end-to-end training of parametric molecular models using deep neural networks. Through multiple experiments, we show that our method is efficient and flexible and performs well on challenging chemical and bio-molecular systems.
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Research Article| November 05 2020
Deep learning for variational multiscale molecular modeling
Jun Zhang ;
Yi Isaac Yang ;
Yi Qin Gao
Yi Qin Gao a)
Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, 518055 Shenzhen,
Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, 100871 Beijing,
Beijing Advanced Innovation Center for Genomics, Peking University, 100871 Beijing,
Biomedical Pioneering Innovation Center, Peking University, 100871 Beijing,
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Jun Zhang, Yao-Kun Lei, Yi Isaac Yang, Yi Qin Gao; Deep learning for variational multiscale molecular modeling. J. Chem. Phys. 7 November 2020; 153 (17): 174115. https://doi.org/10.1063/5.0026836
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