论文标题
自适应带：用于分子模拟中自适应采样的多武器匪徒框架
AdaptiveBandit: A multi-armed bandit framework for adaptive sampling in molecular simulations
论文作者
论文摘要
由于构象空间的尺寸很高，从平衡分布中的取样一直是分子模拟的主要问题。在几十年中，已经使用了许多方法来克服问题。特别是，我们专注于无偏的仿真方法，例如平行和适应性采样。在这里，我们根据多臂匪徒重铸自适应抽样方案，并在此框架下开发一种新颖的自适应采样算法，\ ucb。我们在多个简化电势和蛋白质折叠方案上进行测试。我们发现，与以前的方法相比，该框架在每种类型的测试电位中的性能相似或更好。此外，它提供了一个新型框架，以开发具有更好渐近特征的新采样算法。
Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of conformational space. Over several decades, many approaches have been used to overcome the problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, we recast adaptive sampling schemes on the basis of multi-armed bandits and develop a novel adaptive sampling algorithm under this framework, \UCB. We test it on multiple simplified potentials and in a protein folding scenario. We find that this framework performs similarly or better in every type of test potentials compared to previous methods. Furthermore, it provides a novel framework to develop new sampling algorithms with better asymptotic characteristics.
