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Iterative Unbiasing of Quasi-Equilibrium Sampling.

F Giberti¹, B Cheng², G A Tribello³

¹Laboratory of Computational Science and Modeling, Institute of Materials , École Polytechnique Fédérale de Lausanne , 1015 Lausanne , Switzerland.

Journal of Chemical Theory and Computation

|November 20, 2019

Summary

This summary is machine-generated.

This study introduces a novel iterative unbiasing scheme to efficiently recover accurate molecular dynamics simulations. The method accelerates the study of rare events by effectively reweighting biased data without grid evaluation.

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Area of Science:

Computational Chemistry
Materials Science
Statistical Mechanics

Background:

Atomistic modeling of rare events faces long simulation times due to high free energy barriers.
Bias potentials accelerate phase space exploration but distort microstate distributions.
Efficient reweighting is challenging for time-dependent biases in adaptive sampling methods.

Purpose of the Study:

To develop an efficient iterative unbiasing scheme for adaptive sampling techniques.
To enable accurate recovery of unbiased distributions from biased molecular dynamics simulations.
To overcome limitations of grid-based reweighting methods, especially for high-dimensional biases.

Main Methods:

An iterative unbiasing scheme utilizing all trajectory data.
A novel approach that avoids grid-based distribution evaluation.
Benchmarking against existing reweighting schemes on model systems.

Main Results:

The proposed iterative unbiasing scheme efficiently recovers unbiased distributions.
The method is effective even with high-dimensional bias potentials.
Demonstrated performance across model systems of varying complexity and dimensionality.

Conclusions:

The iterative unbiasing scheme offers a robust and efficient solution for analyzing biased molecular dynamics simulations.
This method significantly enhances the study of rare events in atomistic modeling.
The approach is versatile and applicable to complex systems and high-dimensional biases.