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  1. Abstract

    Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-establishedd-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physicalmore »interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.

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  2. Free, publicly-accessible full text available November 25, 2022
  3. Free, publicly-accessible full text available October 1, 2022
  4. Low-temperature direct ammonia fuel cells (DAFCs) use carbon-neutral ammonia as a fuel, which has attracted increasing attention recently due to ammonia's low source-to-tank energy cost, easy transport and storage, and wide availability. However, current DAFC technologies are greatly limited by the kinetically sluggish ammonia oxidation reaction (AOR) at the anode. Herein, we report an AOR catalyst, in which ternary PtIrZn nanoparticles with an average size of 2.3 ± 0.2 nm were highly dispersed on a binary composite support comprising cerium oxide (CeO 2 ) and zeolitic imidazolate framework-8 (ZIF-8)-derived carbon (PtIrZn/CeO 2 -ZIF-8) through a sonochemical-assisted synthesis method. The PtIrZnmore »alloy, with the aid of abundant OH ad provided by CeO 2 and uniform particle dispersibility contributed by porous ZIF-8 carbon (surface area: ∼600 m 2 g −1 ), has shown highly efficient catalytic activity for the AOR in alkaline media, superior to that of commercial PtIr/C. The rotating disk electrode (RDE) results indicate a lower onset potential (0.35 vs. 0.43 V), relative to the reversible hydrogen electrode at room temperature, and a decreased activation energy (∼36.7 vs. 50.8 kJ mol −1 ) relative to the PtIr/C catalyst. Notably, the PtIrZn/CeO 2 -ZIF-8 catalyst was assembled with a high-performance hydroxide anion-exchange membrane to fabricate an alkaline DAFC, reaching a peak power density of 91 mW cm −2 . Unlike in aqueous electrolytes, supports play a critical role in improving uniform ionomer distribution and mass transport in the anode. PtIrZn nanoparticles on silicon dioxide (SiO 2 ) integrated with carboxyl-functionalized carbon nanotubes (CNT–COOH) were further studied as the anode in a DAFC. A significantly enhanced peak power density of 314 mW cm −2 was achieved. Density functional theory calculations elucidated that Zn atoms in the PtIr alloy can reduce the theoretical limiting potential of *NH 2 dehydrogenation to *NH by ∼0.1 V, which can be attributed to a Zn-modulated upshift of the Pt–Ir d-band that facilitates the N–H bond breakage.« less
  5. Abstract Building upon the d -band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d -states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronicmore »descriptors for the prediction of novel catalytic materials.« less