论文标题
生物分子系统的分子动力学模拟中的机器学习
Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems
论文作者
论文摘要
机器学习（ML）已成为科学，工程及其他地区的普遍工具。它的成功还导致了几种分子动力学（MD）模拟的协同作用，我们用来识别和表征分子系统的主要亚稳态状态。通常，我们旨在确定这些状态的相对稳定性以及它们互换的速度。该信息允许对分子机制的机械描述，可以与实验进行定量比较，并促进其理性设计。 ML影响MD模拟的所有方面 - 从分析数据和加速采样到定义更有效或更准确的仿真模型。
Machine learning (ML) has emerged as a pervasive tool in science, engineering, and beyond. Its success has also led to several synergies with molecular dynamics (MD) simulations, which we use to identify and characterize the major metastable states of molecular systems. Typically, we aim to determine the relative stabilities of these states and how rapidly they interchange. This information allows mechanistic descriptions of molecular mechanisms, enables a quantitative comparison with experiments, and facilitates their rational design. ML impacts all aspects of MD simulations -- from analyzing the data and accelerating sampling to defining more efficient or more accurate simulation models.
