The Adaptive Biasing Force Method: Everything You Always Wanted To Know but Were Afraid To Ask

By Jeffrey Comer, James C. Gumbart, J'erome H'enin, Tony Lelièvre, Andrew Pohorille, Christophe Chipot. Journal of Physical Chemistry B, 2014

437 citations recorded by the source corpus.

In the host of numerical schemes devised to calculate free energy differences by way of geometric transformations, the adaptive biasing force algorithm has emerged as a promising route to map complex free-energy landscapes. It relies upon the simple concept that as a simulation progresses, a continuously updated biasing force is added to the equations of motion, such that in the long-time limit it yields a Hamiltonian devoid of an average force acting along the transition coordinate of interest. This means that sampling proceeds uniformly on a flat free-energy surface, thus providing reliable free-energy estimates. Much of the appeal of the algorithm to the practitioner is in its physically intuitive underlying ideas and the absence of any requirements for prior knowledge about free-energy landscapes. Since its inception in 2001, the adaptive biasing force scheme has been the subject of considerable attention, from in-depth mathematical analysis of convergence properties to novel developments and extensions. The method has also been successfully applied to many challenging problems in chemistry and biology. In this contribution, the method is presented in a comprehensive, self-contained fashion, discussing with a critical eye its properties, applicability, and inherent limitations, as well as introducing novel extensions. Through free-energy calculations of prototypical molecular systems, many methodological aspects are examined, from stratification strategies to overcoming the so-called hidden barriers in orthogonal space, relevant not only to the adaptive biasing force algorithm but also to other importance-sampling schemes. On the basis of the discussions in this paper, a number of good practices for improving the efficiency and reliability of the computed free-energy differences are proposed.

Subfields: Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/a0126094ece504cebe66ff12e8ebbcc63683e2d1

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. (2016, methodology): “An advantage of force-based approaches is that they rely on gradients of the PMF which are defined locally, and hence they can be combined with biasing algorithms such as ABF [66, 88].”
Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0] (2022, methodology): “In Refs [369, 370], multiple windows (localized) umbrella sampling is used to bias some chosen CV while within each umbrella window, metadynamics is used to bias another set of CVs.”