To address challenges in connecting simple descriptors with complicated experimental catalysts, we developed an approach grounded in the Sabatier principle that aggregates adsorption energies over many active sites on different surfaces across a chemical system. We evaluated the usefulness of this relationship by comparing hydrogen adsorption energies predicted by machine-learned DFT surrogate models to hydrogen evolution reaction rates observed in a high-throughput experimental workflow outlined in previous work. In doing so, we discovered that thermodynamically favorable hydrogen adsorption sites are necessary for rapid hydrogen evolution but do not guarantee it, and we then outlined other factors, including poisoning, variation in nucleation and growth, surface expression, and strain effects, that may be modulating this relationship. This work extends the use of adsorption energy calculations as a useful catalyst screening tool to complex nanoparticle systems and increases demand for tools to understand other aspects of the catalytic process. The model developed in this work extends catalyst design principles to large real-world datasets, representing an important step on the path to high-throughput in-the-loop materials discovery. Individual nanoparticle experiments can form materials with many compositions, structures, sizes, and surfaces that may show radically different catalyst activities, and we often cannot know beforehand what these distributions will look like. Materials discovery and design workflows need better strategies for modeling and learning these distributions automatically, which requires adapting and scaling well-known relationships as in this work. In particular, an understanding of the thermodynamic stability of materials and surfaces in different chemical environments will be important not only for understanding experimental observations but also in developing long-lasting catalysts for industrial use. Ultimately, as more advanced models are able to fit gaps in our current analytical understanding, they will ideally be integrated with experiments in closed-loop discovery workflows, accelerating and systematizing catalysis science. The result in this work adds to a growing body of knowledge demonstrating that machine learning can be used to aid discovery by finding complex overlapping trends in large datasets. Because they make numerical predictions, such models can give insight into the scope and accuracy of current understanding in a way that qualitative explanations do not, and deviations from fitted models can guide scientists toward areas where new research is most needed. This work considers the ability of a single well-known trend to describe a complex dataset that poses many of the same problems as industrial design; the challenges in proving the presence of a well-established relationship in such a dataset point to the need for additional computational work to describe other physical phenomena in high throughput.