Abstract
Molecular
simulations are widely applied in the study of chemical and bio-physical
systems of interest. 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 take advantage of both strategies. In this
approach, simulations on different scales are executed simultaneously and
benefit mutually from their cross-talks: Accurate coarse-grained (CG) models
can be inferred from the fine-grained (FG) simulations; In turn, FG simulations
can be boosted by the guidance of CG models. Our method grounds on unsupervised
and reinforcement learning, defined by a variational and adaptive training
objective, and allows end-to-end training of parametric models. Through
multiple experiments, we show that our method is efficient and flexible, and
performs well on challenging chemical and bio-molecular systems.