Deep Learning for Variational Multi-Scale Molecular Modeling
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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 cross-talks: 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.
Alexander von Humboldt Fellowship
National Natural Science Foundation of China
National Natural Science Foundation of ChinaFind out more...