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Skewed Thermodynamic Geometry and Optimal Free Energy Estimation
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Free energy differences are a central quantity of interest in physics, chemistry, and biology. We develop design principles that improve the precision and accuracy of free energy estimators, which has potential applications to screening for targeted drug discovery. Specifically, by exploiting the connection between the work statistics of time-reversed protocol pairs, we develop near-equilibrium approximations for moments of the excess work and analyze the dominant contributions to the precision and accuracy of standard nonequilibrium free-energy estimators. Within linear response, minimum-dissipation protocols follow geodesics of the Riemannian metric induced by the Stokes' friction tensor. We find the next-order contribution arises from the rank-3 supra-Stokes' tensor that skews the geometric structure such that minimum-dissipation protocols follow geodesics of a generalized cubic Finsler metric. Thus, near equilibrium the supra-Stokes' tensor determines the leading-order contribution to the bias of bidirectional free-energy estimators.
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Scaling of Stochastic Normalizing Flows in $\mathrm{SU}(3)$ lattice gauge theory
First demonstration that Stochastic Normalizing Flows inherit the linear-with-volume scaling of non-equilibrium MCMC in 4D SU(3) lattice gauge theory, with a factor-of-two efficiency gain.
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