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1. Periodicity in the Electrochemical Dissolution of Transition Metals

   https://doi.org/10.1002/ange.202100337  

   Speck, Florian D. ; Zagalskaya, Alexandra ; Alexandrov, Vitaly ; Cherevko, Serhiy ( May 2021 , Angewandte Chemie)

   Extensive research efforts are currently dedicated to the search for new electrocatalyst materials in which expensive and rare noble metals are replaced with cheaper and more abundant transition metals. Recently, numerous alloys, oxides, and composites with such metals have been identified as highly active electrocatalysts through the use of high‐throughput screening methods with the help of activity descriptors. Up to this point, stability has lacked such descriptors. Hence, we elucidate the role of intrinsic metal/oxide properties on the corrosion behavior of representative 3d, 4d, and 5d transition metals. Electrochemical dissolution of nine transition metals is quantified using online inductively coupled plasma mass spectrometry (ICP‐MS). Based on the obtained dissolution data in alkaline and acidic media, we establish clear periodic correlations between the amount of dissolved metal, the cohesive energy of the metal atoms (E_coh), and the energy of oxygen adsorption on the metal (ΔH_O,ads). Such correlations can support the knowledge‐driven search for more stable electrocatalysts.

    

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2. Strain‐Dependent Activity‐Stability Relations in RuO 2 and IrO 2 Oxygen Evolution Catalysts

   https://doi.org/10.1002/celc.202300659  

   Chaudhary, Payal ; Zagalskaya, Alexandra ; Over, Herbert ; Alexandrov, Vitaly ( December 2023 , ChemElectroChem)

   Strain engineering is an effective strategy in modulating activity of electrocatalysts, but the effect of strain on electrochemical stability of catalysts remains poorly understood. In this work, we combineab initiothermodynamics and molecular dynamics simulations to examine the role of compressive and tensile strain in the interplay between activity and stability of metal oxides considering RuOand IrOas exemplary catalysts. We reveal that although compressive strain leads to improved activity via the adsorbate‐evolving mechanism of the oxygen evolution reaction, even small strains should substantially destabilize these catalysts promoting dissolution of transition metals. In contrast, our results show that the metal oxides requiring tensile strain to promote their catalytic activity may also benefit from enhanced stability. Importantly, we also find that the detrimental effect of strain on electrochemical stability of atomically flat surfaces could be even greater than that of surface defects.

    

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3. Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

   https://doi.org/10.1038/s41467-020-19524-z  

   Wang, Siwen ; Pillai, Hemanth Somarajan ; Xin, Hongliang ( November 2020 , Nature Communications)

   Building upon thed-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 withd-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.

    

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4. Competing itinerant and local spin interactions in kagome metal FeGe

   https://doi.org/10.1038/s41467-023-44190-2  

   Chen, Lebing ; Teng, Xiaokun ; Tan, Hengxin ; Winn, Barry L. ; Granroth, Garrett E. ; Ye, Feng ; Yu, D. H. ; Mole, R. A. ; Gao, Bin ; Yan, Binghai ; et al ( March 2024 , Nature Communications)

   The combination of a geometrically frustrated lattice, and similar energy scales between degrees of freedom endows two-dimensional Kagome metals with a rich array of quantum phases and renders them ideal for studying strong electron correlations and band topology. The Kagome metal, FeGe is a noted example of this, exhibiting A-type collinear antiferromagnetic (AFM) order atT_N ≈ 400 K, then establishes a charge density wave (CDW) phase coupled with AFM ordered moment belowT_CDW ≈ 110 K, and finally forms ac-axis double cone AFM structure aroundT_Canting ≈ 60 K. Here we use neutron scattering to demonstrate the presence of gapless incommensurate spin excitations associated with the double cone AFM structure of FeGe at temperatures well aboveT_CantingandT_CDWthat merge into gapped commensurate spin waves from the A-type AFM order. Commensurate spin waves follow the Bose factor and fit the Heisenberg Hamiltonian, while the incommensurate spin excitations, emerging belowT_Nwhere AFM order is commensurate, start to deviate from the Bose factor aroundT_CDW, and peaks atT_Canting. This is consistent with a critical scattering of a second order magnetic phase transition with decreasing temperature. By comparing these results with density functional theory calculations, we conclude that the incommensurate magnetic structure arises from the nested Fermi surfaces of itinerant electrons and the formation of a spin density wave order.

    

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5. Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks

   https://doi.org/10.1038/s41467-023-36322-5  

   Pillai, Hemanth Somarajan ; Li, Yi ; Wang, Shih-Han ; Omidvar, Noushin ; Mu, Qingmin ; Achenie, Luke E. ; Abild-Pedersen, Frank ; Yang, Juan ; Wu, Gang ; Xin, Hongliang ( December 2023 , Nature Communications)

   The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt₃Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt₃Ir, and Pt₃Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond thed-band center metric of binding sites.

    

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