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Small-sample limit of the Bennett acceptance ratio method and the variationally derived intermediates.

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Reinhardt,  M.
Department of Theoretical and Computational Biophysics, MPI for Biophysical Chemistry, Max Planck Society;

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Grubmüller,  H.
Department of Theoretical and Computational Biophysics, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Reinhardt, M., & Grubmüller, H. (2021). Small-sample limit of the Bennett acceptance ratio method and the variationally derived intermediates. Physical Review E, 104(5): 054133. doi:10.1103/PhysRevE.104.054133.


Cite as: https://hdl.handle.net/21.11116/0000-000A-BDFC-6
Abstract
Free energy calculations based on atomistic Hamiltonians provide microscopic insight into the thermodynamic
driving forces of biophysical or condensed matter systems. Many approaches use intermediate Hamiltonians
interpolating between the two states for which the free energy difference is calculated. The Bennett acceptance
ratio (BAR) and variationally derived intermediates (VI) methods are optimal estimator and intermediate states in
that the mean-squared error of free energy calculations based on independent sampling is minimized. However,
BAR and VI have been derived based on several approximations that do not hold for very few sample points.
Analyzing one-dimensional test systems, we show that in such cases BAR and VI are suboptimal and that
established uncertainty estimates are inaccurate. Whereas for VI to become optimal, less than seven samples
per state suffice in all cases; for BAR the required number increases unboundedly with decreasing configuration
space densities overlap of the end states. We show that for BAR, the required number of samples is related
to the overlap through an inverse power law. Because this relation seems to hold universally and almost
independent of other system properties, these findings can guide the proper choice of estimators for free energy
calculations.