  Free energies play a central role in characterising the behaviour of chemical
systems and are among the most important quantities that can be calculated by
molecular dynamics simulations. The free energy of hydration in particular is a
well-studied physicochemical property of drug-like molecules and is commonly
used to assess and optimise the accuracy of nonbonded parameters in empirical
forcefields, and as a fast-to-compute surrogate of performance for
protein-ligand binding free energy estimation. Machine learned potentials
(MLPs) show great promise as more accurate alternatives to empirical
forcefields, but are not readily decomposed into physically motivated
functional forms, which has thus far rendered them incompatible with standard
alchemical free energy methods that manipulate individual pairwise interaction
terms. However, since the accuracy of free energy calculations is highly
sensitive to the forcefield, this is a key area in which MLPs have the
potential to address the shortcomings of empirical forcefields. In this work,
we introduce an efficient alchemical free energy method compatible with MLPs,
enabling, for the first time, calculations of biomolecular free energy with
\textit{ab initio} accuracy. Using a pretrained, transferrable, alchemically
equipped MACE model, we demonstrate sub-chemical accuracy for the hydration
free energies of organic molecules.
