Here, we solve the problem by using machine learning (ML)-aided FP calculations of free energies. This approach allows for accurate free energy calculations of OH*, taking into account the fluctuations of water molecules, by employing thermodynamic integration (TI) with finite-temperature MD simulations that are accelerated by several orders of magnitude using machine-learned force fields (MLFFs). Errors in the MLFFs can be efficiently corrected by additional TI calculations from the MLFFs to the FP potential energy surfaces, resulting in precise FP free energies. Using these computed free energies, we calculate the surface redox potentials for reducing OH* to water on single-crystal Pt and Pt alloy (111) surfaces, as well as on the Pt(111) surface with melamine molecules. We also evaluate the catalytic activity using a kinetic ORR model with the computed redox potentials as inputs. As demonstrated by the volcano-plot of catalytic activity versus the redox potential of the OH* reduction (5) shown in Fig. , our method reproduces the trend of high activity observed experimentally on the melamine-modified Pt surface. By comparing the activities, redox potentials, and the electronic and solvent structures, we clarify the mechanism of activity enhancement due to the addition of melamine molecules. Another objective of this study is to establish a method for accurately predicting the free energy of reaction intermediates in catalytic reactions at solid-liquid interfaces, relative to the absolute reference of a noninteracting ideal gas, using TI aided by ML. While MLFFs offer fast computation, they can result in large errors and physically meaningless outcomes in extrapolated regions beyond the training data. In this study, we demonstrate a method that leverages the computational speed of MLFFs while ensuring stable thermodynamic integration within the interpolated space of the training data, thereby enabling FP evaluation of interfacial free energy changes at finite temperatures.