This study introduces the P5 model - a foundational method that utilizes reinforcement learning (RL) to augment control, effectiveness, and scalability in molecular dynamics simulations (MD). Our innovative strategy optimizes the sampling of target polymer chain conformations, marking an efficiency improvement of over 37.1%. The RL-induced control policies function as an inductive bias, modulating Brownian forces to steer the system towards the preferred state, thereby expanding the exploration of the configuration space beyond what traditional MD allows. This broadened exploration generates a more varied set of conformations and targets specific properties, a feature pivotal for progress in polymer development, drug discovery, and material design. Our technique offers significant advantages when investigating new systems with limited prior knowledge, opening up new methodologies for tackling complex simulation problems with generative techniques.

翻译：本研究提出了P5模型——一种利用强化学习（RL）增强分子动力学模拟（MD）中控制性、有效性和可扩展性的基础方法。我们的创新策略优化了目标聚合物链构象的采样，实现了超过37.1%的效率提升。RL诱导的控制策略作为归纳偏置，通过调节布朗力将系统导向偏好状态，从而将构象空间的探索范围扩展到传统MD方法之外。这种扩展的探索产生了更多样化的构象集，并针对特定属性进行定向优化，这一特性对聚合物开发、药物发现和材料设计领域的进展至关重要。我们的技术在对先验知识有限的新系统进行研究时具有显著优势，为通过生成式技术解决复杂模拟问题开辟了新方法。