Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the<br>accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems<br>containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either<br>sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging<br>coarse-grained models. Although both strategies are promising, either of them, if adopted individually,<br>exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so<br>that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)<br>models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG<br>simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method<br>defines a variational and adaptive training objective which allows end-to-end training of parametric<br>molecular models using deep neural networks. Through multiple experiments, we show that our method is<br>efficient and flexible, and performs well on challenging chemical and bio-molecular systems.
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Inference of Hidden Structures in Complex Physical Systems by Multi-scale Clustering
