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Direct electrochemical production of dissolved ozone could potentially provide economic wastewater treatment and sanitation or a valuable chemical oxidant. Although Ni‐Sb‐SnO₂electrocatalysts have the highest known faradaic efficiencies for electrochemical ozone production, the activity and selectivity are not yet sufficient for commercial implementation. This work finds that co‐doping Ni and Gd increases the ozone selectivity by a factor of three over Ni alone. These findings are the first demonstration of an active dopant other than Ni in SnO₂. Electrochemical and physical characterization show that trends in ozone activity are caused by chemical catalysis, not morphology effects, and that conduction band alignment is not a catalytic descriptor for the system. Selective radical quenching experiments and quantum chemistry calculations of thermodynamic energies suggest that the kinetic barriers to form solution‐phase intermediates are important for understanding the role of dopants in electrochemical ozone production.

 

Alchemical perturbation density functional theory (APDFT) has promise for enabling computational screening of hypothetical catalyst sites. Here, we analyze errors in first order APDFT calculation schemes for binding energies of CH_x, NH_x, OH_x, and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then train three different support vector regression machine learning models that correct systematic APDFT prediction errors for each of the three classes of carbon, nitrogen, and oxygen based adsorbates. While uncorrected first order APDFT alone approximates accurate adsorbate binding energies on up to 36 hypothetical alloys based on a single Kohn–Sham DFT calculation on a 3 × 3 unit cell for Pt(111), the machine learning‐corrected APDFT extends this number to more than 20,000 and provides a recipe for developing other machine learning‐based APDFT models.

 

Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms, but the employed global descriptor limits transferability to ensembles of molecules. Many-body expansions (MBEs) should provide a rigorous procedure for size-transferable GDML by training models on fundamental n-body interactions. We developed many-body GDML (mbGDML) force fields for water, acetonitrile, and methanol by training 1-, 2-, and 3-body models on only 1000 MP2/def2-TZVP calculations each. Our mbGDML force field includes intramolecular flexibility and intermolecular interactions, providing that the reference data adequately describe these effects. Energy and force predictions of clusters containing up to 20 molecules are within 0.38 kcal/mol per monomer and 0.06 kcal/(mol Å) per atom of reference supersystem calculations. This deviation partially arises from the restriction of the mbGDML model to 3-body interactions. GAP and SchNet in this MBE framework achieved similar accuracies but occasionally had abnormally high errors up to 17 kcal/mol. NequIP trained on total energies and forces of trimers experienced much larger energy errors (at least 15 kcal/mol) as the number of monomers increased—demonstrating the effectiveness of size transferability with MBEs. Given these approximations, our automated mbGDML training schemes also resulted in fair agreement with reference radial distribution functions (RDFs) of bulk solvents. These results highlight mbGDML as valuable for modeling explicitly solvated systems with quantum-mechanical accuracy.