The discovery of new catalysts for fundamental reactions such as water splitting, ammonia synthesis, or CO 2 reduction is a fundamental to the development of an environmentally sustainable economy. By increasing reaction efficiency, catalysts offer the possibility of reducing industrial energy requirements and enabling more sustainable chemical production processes . However, the space of all possible catalysts is too vast to be surveyed experimentally . For this reason, the field of computational catalysis tries to predict catalytic activity using materials properties. One important tool is the Sabatier principle, which describes a volcano-shaped relationship between the binding energy of a key reaction specie and the overall reaction rate . Although this relationship was developed using experimental binding energies , these values can be computed using Density Functional Theory (DFT), a computationally tractable approximation to quantum chemistry . Modern efforts to computationally screen chemical composition space for new catalysts began almost two decades ago. Nørskov et al. established a kinetic model that uses DFT-calculated ∆E(*H) on an fcc site to predict exchange current density. This work also considered the effects of changing coverage and found them to be minimal. Later efforts by the same group looked at multiple adsorption sites in more detail, screening bimetallic alloys modeled as a pure metal with a secondary metal incorporated into the surface layer and further developing the kinetic model (see Figure ). The latter work used 1 monolayer coverage for all materials on the left half of the volcano. The researchers also made the observation that, despite the fact that many of the metals on the left-hand side of the volcano should be oxidized, experimental values align with adsorption calculations made using metallic surfaces. The development of computationally grounded volcano plots has spurred researchers to find ways to optimize and streamline their use in catalytic discovery. Tran and Ulissi (2018) established a high-throughput automated screening method using active learning to discover materials with binding energies that indicate activity for hydrogen evolution and CO 2 reduction . Another effort established a data set of DFT calculations and associated community challenges to spur interest in machine learning models capable of approximating DFT adsorption energies across a variety of adsorbates and materials for catalyst discovery . However, several complications differentiate industrial catalysts from the slab models commonly used to compute adsorption energies . Modern industrial catalysts are often highly nanostructured, experiencing size-specific chemical phenomena that impact catalysis , and nanoparticles may undergo surface or bulk segregation to form a distribution of structures and surfaces in practice . Furthermore, it is not always clear which surfaces and binding sites are most responsible for catalytic activity, which is confounded by the difficulty of experimentally characterizing structure or surface expression at the scale of modern computational and experimental screens . Bimetallic nanoparticles have been of interest for their catalytic properties for many reactions in recent decades . The expanded bimetallic material search space offers the possibility of discovering cheaper or more active catalysts while remaining tractable using modern computational techniques . In previous work, we developed a high-throughput method for synthesizing bimetallic nanoparticles and measuring their ability to catalyze the Hydrogen Evolution Reaction (HER) . In this procedure, metal salts are combined with a photocatalyst, and light stimulates the reduction of ions into nanoparticles and the subsequent evolution of hydrogen gas (see Fig. ). In this work, we assess the sufficiency of hydrogen adsorption energy predictions in describing the resulting data: the experimental HER activities of bimetallic nanoparticles are compared with a computational workflow that predicts and aggregates hydrogen adsorption energies across various surfaces from intermetallic crystal structures from the Materials Project . Our computational workflow is distinguished from previous efforts because it automates the process of predicting adsorption energies across different binding sites, surfaces, and materials, allowing catalysis experiment and theory to be compared at greater scales than previously possible.