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Andrew Pohorille

Researcher at Ames Research Center

Publications -  124
Citations -  9234

Andrew Pohorille is an academic researcher from Ames Research Center. The author has contributed to research in topics: Membrane & Molecular dynamics. The author has an hindex of 44, co-authored 122 publications receiving 8620 citations. Previous affiliations of Andrew Pohorille include University of California, San Francisco & University of California.

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Calculating free energies using average force

TL;DR: In this article, a general formula that connects the derivatives of the free energy along the selected, generalized coordinates of the system with the instantaneous force acting on these coordinates is derived, defined as the forces acting on the coordinate of interest so that when it is subtracted from the equations of motion the acceleration along this coordinate is zero.
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Adaptive biasing force method for scalar and vector free energy calculations.

TL;DR: The approach based on time derivatives can be combined with the adaptive biasing force method, an enhanced sampling technique that rapidly yields uniform sampling of the order parameters, and by doing so greatly improves the efficiency of free energy calculations.
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An information theory model of hydrophobic interactions.

TL;DR: This model is shown to account quantitatively for the central hydrophobic phenomena of cavity formation and association of inert gas solutes and the simplicity and flexibility of the approach suggest that it should permit applications to conformational equilibria of nonpolar solute andhydrophobic residues in biopolymers.
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Good Practices in Free-Energy Calculations

TL;DR: The current best practices for carrying out free- energy calculations using free energy perturbation and nonequilibrium work methods are discussed, demonstrating that at little to no additional cost, free-energy estimates could be markedly improved and bounded by meaningful error estimates.
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The adaptive biasing force method: everything you always wanted to know but were afraid to ask.

TL;DR: In this contribution, the adaptive biasing force algorithm 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.