To illustrate the integration of theory, machine learning, and experiment, we describe advancements in identifying promising electrocatalysts for the removal of the harmful oxyanions in water . In particular, we focus on nitrate pollution; resulting from agricultural and industrial sources, nitrate can cause serious environmental and health problems such as eutrophication and methemoglobinemia . Liu et al. previously developed volcano activity plots for transition metal electrocatalysts toward electroreduction of nitrate based on simple thermodynamic descriptors, e.g., binding energy of atomic oxygen and nitrogen . Moreover, they have successfully validated the computational predictions on the activity and selectivity of platinum-ruthenium alloys (i.e., PtxRuy) for nitrate reduction , exhibiting 93 -98% Faradaic efficiencies toward NH3 production at 0.1 V vs. RHE. The calculated trend corresponded well with subsequent experimental results, showing the catalytic activity of Rh/C electrodes can be enhanced by Sn modification . These studies highlight the kind of synergy that can exist between the experimental and computational results. To rapidly validate the theoretical and ML calculations, we have developed an integrated platform for the synthesis and characterization of electrocatalysts toward electro-reduction of oxyanions (Figure ). For the synthesis of metal nanoparticles (NPs), a chemical reduction reaction was adopted on a versatile liquid handling robot. This robot can handle 6 to 96 reactions at temperatures up to 95 °C with shaking speed up to 2500 rpm. Following synthesis, the electrochemical screening platform comprises two steps: (1) initial cyclic voltammetry (CV) screening and (2) subsequent chronoamperometry (CA) screening, with test durations ranging from minutes to hours.