The Jarzynski equality is one of the most widely celebrated and scrutinized nonequilibrium work theorems, relating free energy to the external work performed in nonequilibrium transitions. In practice, the required ensemble average of the Boltzmann weights of infinite nonequilibrium transitions is estimated as a finite sample average, resulting in the so-called Jarzynski estimator, ΔFJ. Alternatively, the second-order approximation of the Jarzynski equality, though seldom invoked, is exact for Gaussian distributions and gives rise to the Fluctuation-Dissipation estimator ΔFFD. Here we derive the parametric maximum-likelihood estimator (MLE) of the free energy ΔFML considering unidirectional work distributions belonging to Gaussian or Gamma families, and compare this estimator to ΔFJ. We further consider bidirectional work distributions belonging to the same families, and compare the corresponding bidirectional ΔFML* to the Bennett acceptance ratio (ΔFBAR) estimator. We show that, for Gaussian unidirectional work distributions, ΔFFD is in fact the parametric MLE of the free energy, and as such, the most efficient estimator for this statistical family. We observe that ΔFML and ΔFML* perform better than ΔFJ and ΔFBAR, for unidirectional and bidirectional distributions, respectively. These results illustrate that the characterization of the underlying work distribution permits an optimal use of the Jarzynski equality.

On the accurate estimation of free energies using the Jarzynski equality

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