calculations and an RBFE calculation. 68 Time allocation was based on the time derivatives of the variance of the free energy estimates between adjacent λ windows obtained with the Bennett Acceptance Ratio for a single run. However, this did not substantially improve the efficiency of the RBFE calculation due to relatively short and similar autocorrelation times of the perturbed energy differences between windows. A similar approach is more likely to benefit ABFE calculations, which are more susceptible to dramatic variations in correlation time between windows as a result of sampling issues. Indeed, Mendoza-Martinez et al. demonstrated that the equilibration of ABFE calculations could be accelerated by allocating sampling time based on the uncertainties of the inter-window free energy changes. In contrast to Sun et al., errors were calculated from the difference between replicate runs, which is a robust method for uncertainty quantification. However, this protocol lacks a rigorous derivation, and no automated implementation is available. While this work focuses on the adaptive allocation of sampling time within single ABFE calculations, Li et al. showed how adaptively allocating simulation time to individual calculations within a network could substantially improve statistical precision. The final area is the detection of equilibration and convergence. Intermediate state simulations in free energy calculations are usually started from coordinates sampled with the ligand fully interacting. These coordinates may have a very low probability under the equilibrium distributions of other states, producing initial transients in the estimated free energy changes as the system relaxes. It is standard practice to discard samples from these equilibration (burn-in) periods to avoid introducing bias to the final free energy estimate. Convergence refers to the approach of the free energy estimate to its asymptotic (infinite sampling) value. Methods to assess equilibration and convergence are essential to increase confidence that finite-time simulations are representative of the underlying stationary distributions. However, it is never possible to state this with certainty. In free energy calculations, the most common equilibration detection protocols are those of Chodera and Yang et al.. Yang et al. monitored the behaviour of the reverse cumulative average of the free energy gradient. Contamination by non-equilibrated data was detected by deviation from normality. Chodera proposed a non-parametric method which selects the unequilibrated region by maximising the effective sample size of the production region.