CO2 electrochemical reduction to formate has emerged as one of the promising routes for CO2 conversion to useful chemicals and renewable energy storage. Palladium has been shown to make formate with a high selectivity at minimal overpotential. However, production of CO as a minor product quickly deactivates the catalyst during extended electrolysis. Here, we present nanoporous Pd alloys (np-PdX) formed by electrochemical dealloying of Pd15X85 alloys (X = Co, Ni, Cu, and Ag) as active free standing electrocatalysts with high formate selectivity and superior CO poisoning tolerance. Rate of deactivation under constant potential electrolysis, due to CO passivation, is strongly correlated to the identity of the transition metal alloying component. We purport that this composition dependent behavior is due to the induced electronic changes in the active Pd surface, affecting both the CO adsorption strength and the near surface hydrogen 2 solubility which can impact the adsorption strength of active/inactive intermediates and reaction selectivity. Free-standing np-PdCo is found to exhibit high areal formate partial current densities, > 40 mA cm-2 , with superior CO poisoning tolerance and minimal active area loss at cathodic potentials, demonstrating the utility of these materials for selective and stable CO2 electrolysis.

Computational catalysis plays a growingly important role in guiding the design of new and improved materials for catalysis. [1] Candidates for catalyst sites are regularly screened for using high level quantum chemistry calculations, [2] [3] [4] [5] and Kohn-Sham density functional theory (KS-DFT) is normally applied because it brings a favorable balance of accuracy, transferability, and computational efficiency. However, even the simplest KS-DFT calculations can be relatively computationally intensive, and their computational expense limits their utility in very deep searches through hypothetical materials space. One way to expedite catalyst discovery is with the d-band model, [6] which relates an adsorbate's binding energy (BE) with the position of the catalyst surface's d-band center. If a shift in the d-band center from a reference state to a hypothetical one is known, that shift would correlate with the difference between an adsorbate's BE in the two cases. While elegant and useful, the d-band model does not satisfactorily predict trends in calculated BEs for electronegative adsorbates such as OH, F, and Cl on materials having mostly filled dstates. [7] Furthermore, the d-band model can only be used on systems that have significant adsorption energy contributions that arise from d-orbitals, i.e. transition metal systems. For other classes of materials, extensions to the d-band model have been developed. [8] [9] [10] Analogous to the d-band model, computational alchemy correlates an adsorbate's BE to the material's electrostatic potentials, [11] [12] [13] which in effect reflects an amalgam of the

The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme, called alchemical perturbation density functional theory (APDFT), for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on the Kohn-Sham density functional theory (DFT) reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present benchmarking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH 4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are performed, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations. K E Y W O R D S adsorption energies, barrier heights, binding energies, computational catalysis, density functional theory, nudged elastic band calculations 1 | INTRODUCTION Advances in computational chemistry open new possibilities for impressively large-scale computational screening of hypothetical catalysts across materials space. [1-3] However, productively leveraging high-throughput screening has been challenging. For useful and insightful predictions, computational screening studies must be reproducible while also (a) determining important active sites that are stable under specified environmental conditions on large numbers of material compositions and (b) elucidating important elementary reaction steps with barrier heights that are

<p>Kohn-Sham density functional theory (DFT)-based searches for hypothetical catalysts are too computationally demanding for wide searches through diverse materials space. Our group has been critically evaluating the performance of an alternative computational method called computational alchemy. An advantage with this method is that it effectively brings no computational cost once a single DFT reference calculation is made. Extending from our 2017 publication in <i>J. Phys. Chem. Lett </i>(DOI: 10.1021/acs.jpclett.7b01974) that tested computational alchemy for transition metal alloys, we now assess the accuracy of computational alchemy schemes on carbides, nitrides, and oxides. </p>

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. K E Y W O R D S adsorption, binding energies, high throughput screening 1 | INTRODUCTION Many efforts in computational catalysis are focused on screening materials for innovative catalysts that promote high chemical activity. 1-3 Descriptors for catalyst activity, for example, an adsorbate binding