Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamical behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of phase space. In this work, we present a framework for identifying the optimal sampling policy through metric driven ranking. Our approach systematically evaluates the policy ensemble and ranks the policies based on their ability to explore the conformational space effectively. Through a series of biomolecular simulation case studies, we demonstrate that choice of a different adaptive sampling policy at each round significantly outperforms single policy sampling, leading to faster convergence and improved sampling performance. This approach takes an ensemble of adaptive sampling policies and identifies the optimal policy for the next round based on current data. Beyond presenting this ensemble view of adaptive sampling, we also propose two sampling algorithms that approximate this ranking framework on the fly. The modularity of this framework allows incorporation of any adaptive sampling policy making it versatile and suitable as a comprehensive adaptive sampling scheme.

翻译：在生物分子模拟中，高效采样对于准确捕捉生物系统的复杂动力学行为至关重要。自适应采样技术旨在通过将计算资源集中于相空间中最相关的区域来提高效率。本文提出了一种通过度量驱动排序来识别最优采样策略的框架。该方法系统评估策略集合，并根据各策略在有效探索构象空间方面的能力对其进行排序。通过一系列生物分子模拟案例研究，我们证明在每一轮选择不同的自适应采样策略显著优于单一策略采样，能够实现更快的收敛速度和改进的采样性能。该方法基于当前数据，从自适应采样策略集合中识别出下一轮的最优策略。除了提出这种自适应采样的集合视角，我们还提出了两种能够实时近似该排序框架的采样算法。该框架的模块化特性允许整合任何自适应采样策略，使其具有通用性，适合作为一种全面的自适应采样方案。