Redox potentials of electron transfer reactions are of fundamental importance
for the performance and description of electrochemical devices. Despite decades
of research, accurate computational predictions for the redox potential of even
simple metals remain very challenging. Here we use a combination of first
principles calculations and machine learning to predict the redox potentials of
three redox couples, Fe2+/Fe3+,
Cu+/Cu2+ and Ag+/Ag2+.
Using a hybrid functional with a fraction of 25\% exact exchange (PBE0) the
predicted values are 0.92, 0.26 and 1.99 V in good agreement with the best
experimental estimates (0.77, 0.15, 1.98 V). We explain in detail, how we
combine machine learning, thermodynamic integration from machine learning to
semi-local functionals, as well as a combination of thermodynamic perturbation
theory and Δ-machine learning to determine the redox potentials for
computationally expensive hybrid functionals. The combination of these
approaches allows one to obtain statistically accurate results