Multiscale simulations are a well-accepted way to bridge the length and time scales required for scientific studies with the solution accuracy achievable through available computational resources. Traditional approaches either solve a coarse model with selective refinement or coerce a detailed model into faster sampling, both of which have limitations. Here, we present a paradigm of adaptive, multiscale simulations that couple different scales using a dynamic-importance sampling approach. Our method uses machine learning to dynamically and exhaustively sample the phase space explored by a macro model using microscale simulations and enables an automatic feedback from the micro to the macro scale, leading to a self-healing multiscale simulation. As a result, our approach delivers macro length and time scales, but with the effective precision of the micro scale. Our approach is arbitrarily scalable as well as transferable to many different types of simulations. Our method made possible a multiscale scientific campaign of unprecedented scale to understand the interactions of RAS proteins with a plasma membrane in the context of cancer research running over several days on Sierra, which is currently the second-most-powerful supercomputer in the world.

多尺度模拟是一种广为接受的方法，可以将科学研究所需的长度和时间尺度与通过可用计算资源可实现的解决方案精度联系起来。传统方法要么通过选择性细化来解决粗略模型，要么将详细模型强制为更快的采样，这两者都有局限性。在这里，我们提出了一种自适应多尺度模拟范式，该范式使用动态重要性采样方法耦合不同尺度。我们的方法使用机器学习来动态和详尽地对使用微观模拟的宏观模型探索的相空间进行采样，并实现从微观到宏观的自动反馈，从而实现自我修复的多尺度模拟。因此，我们的方法提供了宏观长度和时间尺度，但具有微观尺度的有效精度。我们的方法是任意可扩展的，并且可以转移到许多不同类型的模拟中。我们的方法使一项前所未有的多尺度科学活动成为可能，以了解 RAS 蛋白与质膜在癌症研究背景下的相互作用，Sierra 目前是世界上第二强大的超级计算机。