Buckets Instead of Umbrellas for Enhanced Sampling and Free Energy Calculations

Umbrella sampling has been a workhorse for free energy calculations in molecular simulations for several decades. In conventional umbrella sampling, restraining bias potentials are strategically applied along one or several collective variables. Major drawbacks associated with this method are the requirement of a large number of bias windows and the poor sampling of the transverse coordinates. In this work, we propose an alternate formalism that departs from the traditional umbrella sampling to mitigate these issues, where we replace umbrella-type restraining bias potentials with bucket-type wall potentials. This modification permits one to formulate an efficient computational strategy leveraging wall potentials and metadynamics sampling. This new method, called "bucket sampling", can significantly reduce the computational cost of obtaining converged high-dimensional free energy surfaces. Extensions of the proposed method with temperature acceleration and replica exchange solute tempering are also demonstrated.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2024-09-30
DOI
https://doi.org/10.1021/acs.jctc.4c00776
Citations
6
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.69

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article

Buckets Instead of Umbrellas for Enhanced Sampling and Free Energy Calculations

Anji Babu Kapakayala, Ramsha Javed, Nisanth N. Nair
6 citations
Journal of Chemical Theory and Computation
Model Reduction and Neural Networks
1.69
article

Buckets Instead of Umbrellas for Enhanced Sampling and Free Energy Calculations

Anji Babu Kapakayala, Ramsha Javed, Nisanth N. Nair
article en
6 citations

Abstract

Umbrella sampling has been a workhorse for free energy calculations in molecular simulations for several decades. In conventional umbrella sampling, restraining bias potentials are strategically applied along one or several collective variables. Major drawbacks associated with this method are the requirement of a large number of bias windows and the poor sampling of the transverse coordinates. In this work, we propose an alternate formalism that departs from the traditional umbrella sampling to mitigate these issues, where we replace umbrella-type restraining bias potentials with bucket-type wall potentials. This modification permits one to formulate an efficient computational strategy leveraging wall potentials and metadynamics sampling. This new method, called "bucket sampling", can significantly reduce the computational cost of obtaining converged high-dimensional free energy surfaces. Extensions of the proposed method with temperature acceleration and replica exchange solute tempering are also demonstrated.

Journal of Chemical Theory and ComputationVol. 20(19)
Indian Institute of Technology Kanpur (IN)
Indian Institute of Technology Kanpur
Affordable and clean energy
Openalex Percentile: Top 16%
Model Reduction and Neural Networks
1.69
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