A valuable step in the modeling of multiscale dynamical systems in fields
such as computational chemistry, biology, materials science and more, is the
representative sampling of the phase space over long timescales of interest;
this task is not, however, without challenges. For example, the long term
behavior of a system with many degrees of freedom often cannot be efficiently
computationally explored by direct dynamical simulation; such systems can often
become trapped in local free energy minima. In the study of physics-based
multi-time-scale dynamical systems, techniques have been developed for
enhancing sampling in order to accelerate exploration beyond free energy
barriers. On the other hand, in the field of Machine Learning, a generic goal
of generative models is to sample from a target density, after training on
empirical samples from this density. Score based generative models (SGMs) have
demonstrated state-of-the-art capabilities in generating plausible data from
target training distributions. Conditional implementations of such generative
models have been shown to exhibit significant parallels with long-established
-- and physics based -- solutions to enhanced sampling. These physics-based
methods can then be enhanced through coupling with the ML generative models,
complementing the strengths and mitigating the weaknesses of each technique. In
this work, we show that that SGMs can be used in such a coupling framework to
improve sampling in multiscale dynamical systems.

在多尺度动力学系统中，通过代表性采样长时间尺度的相空间是一项有价值的工作，但也面临挑战。我们展示了基于评分的生成模型可以在这种耦合框架中用于提高多尺度动力学系统的采样效果。