We develop a theoretical framework for a priori estimation of catalytic activity of metal nanoparticles using geometry-based reactivity descriptors of surface atoms and kinetic analysis of reaction pathways at various types of active sites. We show that orbitalwise coordination numbers 𝐶𝑁^α (α = 𝑠 or 𝑑) can be used to predict chemical reactivity of a metal site (e.g., adsorption energies of critical reaction intermediates) by being aware of the neighboring chemical environment, outperforming their regular (𝐶𝑁) and generalized (𝐶̅𝑁̅) counterparts with little added computational cost. Here we include two examples to illustrate this method: CO oxidation on Au (5𝑑¹⁰6𝑠¹) and O₂ reduction on Pt (5𝑑⁹6𝑠¹). We also employ Bayesian learning and the Newns-Anderson model to advance the fundamental understanding of adsorbate-surface interactions on metal nanocatalysts, paving the path toward adsorbate-specific tuning of catalysis. / Doctor of Philosophy / The interactions between reaction intermediates and catalysts should be neither too strong nor too weak for catalytic optimization. This Sabatiers principle arising from the scaling relations among the energetics of reacting species at geometrically similar sites, provides the conceptual basis for designing improved catalysts, but imposes volcano-type limitations on the attainable catalytic activity and selectivity. One of the greatest challenges faced by the catalysis community today is how to develop design strategies and ultimately predictive models of catalytic systems that could circumvent energy scaling relations. This work brings the quantum-chemical modeling and machine learning technique together and develops a novel stochastic modeling approach to rationally design the catalysts with desired properties and bridges our knowledge gap between the empirical kinetics and atomistic mechanisms of catalytic reactions.