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AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular Simulations.
Journal of Chemical Theory and Computation ( IF 5.5 ) Pub Date : 2020-06-15 , DOI: 10.1021/acs.jctc.0c00205
Adrià Pérez 1 , Pablo Herrera-Nieto 1 , Stefan Doerr 1, 2 , Gianni De Fabritiis 1, 2, 3
Affiliation  

Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of the 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, AdaptiveBandit. We test it on multiple simplified potentials and in a protein folding scenario. We find that this framework performs similarly to or better than previous methods in every type of test potential. Furthermore, it provides a novel framework to develop new sampling algorithms with better asymptotic characteristics.

中文翻译:

AdaptiveBandit:用于分子模拟中自适应采样的多臂Bandit框架。

由于构象空间的维数很高,从平衡分布中采样一直是分子模拟中的主要问题。几十年来,已经采用了许多方法来解决该问题。特别是,我们专注于无偏仿真方法,例如并行和自适应采样。在这里,我们基于多臂土匪重塑了自适应采样方案,并在此框架下开发了一种新颖的自适应采样算法AdaptiveBandit。我们在多种简化的电势和蛋白质折叠方案中对其进行测试。我们发现,在每种类型的测试潜力中,该框架的性能均与以前的方法相似或更好。此外,它提供了一个新颖的框架来开发具有更好渐近特性的新采样算法。
更新日期:2020-07-14
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