AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular SimulationsClick to copy article linkArticle link copied!
- Adrià PérezAdrià PérezComputational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, SpainMore by Adrià Pérez
- Pablo Herrera-NietoPablo Herrera-NietoComputational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, SpainMore by Pablo Herrera-Nieto
- Stefan DoerrStefan DoerrComputational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, SpainAcellera Labs, 08005 Barcelona, SpainMore by Stefan Doerr
- Gianni De Fabritiis*Gianni De Fabritiis*Email: [email protected]Computational Science Laboratory, Universitat Pompeu Fabra, 08003 Barcelona, SpainAcellera Labs, 08005 Barcelona, SpainInstitució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, SpainMore by Gianni De Fabritiis
Abstract
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.
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