Computational Science and Engineering Laboratory, ETH Zurich, CH-8092, Switzerland.
Professorship of Multiscale Modeling of Fluid Materials, TUM School of Engineering and Design, Technical University of Munich, 85748 Garching bei München, Germany.
J Chem Theory Comput. 2022 Jan 11;18(1):538-549. doi: 10.1021/acs.jctc.1c00809. Epub 2021 Dec 10.
Simulations are vital for understanding and predicting the evolution of complex molecular systems. However, despite advances in algorithms and special purpose hardware, accessing the time scales necessary to capture the structural evolution of biomolecules remains a daunting task. In this work, we present a novel framework to advance simulation time scales by up to 3 orders of magnitude by learning the effective dynamics (LED) of molecular systems. LED augments the equation-free methodology by employing a probabilistic mapping between coarse and fine scales using mixture density network (MDN) autoencoders and evolves the non-Markovian latent dynamics using long short-term memory MDNs. We demonstrate the effectiveness of LED in the Müller-Brown potential, the Trp cage protein, and the alanine dipeptide. LED identifies explainable reduced-order representations, i.e., collective variables, and can generate, at any instant, all-atom molecular trajectories consistent with the collective variables. We believe that the proposed framework provides a dramatic increase to simulation capabilities and opens new horizons for the effective modeling of complex molecular systems.
模拟对于理解和预测复杂分子系统的演化至关重要。然而，尽管算法和专用硬件取得了进展，但要达到捕捉生物分子结构演化所需的时间尺度仍然是一项艰巨的任务。在这项工作中，我们提出了一种新的框架，通过学习分子系统的有效动力学（LED），将模拟时间尺度提高了 3 个数量级。LED 通过使用混合物密度网络（MDN）自动编码器在粗尺度和细尺度之间进行概率映射，对无方程方法进行了扩充，并使用长短期记忆 MDN 来演化非马尔可夫潜在动力学。我们在 Müller-Brown 势能、色氨酸笼状蛋白和丙氨酸二肽中证明了 LED 的有效性。LED 确定了可解释的降阶表示，即集体变量，并且可以在任何时刻生成与集体变量一致的全原子分子轨迹。我们相信，所提出的框架为模拟能力提供了显著的提高，并为复杂分子系统的有效建模开辟了新的视野。
