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Understanding selectivity trends is a crucial hurdle in the developing innovative catalysts for generating hydrogen peroxide through the two‐electron oxygen reduction reaction (2e‐ORR). The identification of selectivity patterns has been made more accessible through the introduction of a newly developed selectivity descriptor derived from thermodynamics, denoted as ΔΔG introduced in Chem Catal. 2023, 3(3), 100568. To validate the suitability of this parameter as a descriptor for 2e‐ORR selectivity, we utilize an extensive library of 155 binary alloys. We validate that ΔΔG reliably depicts the selectivity trends in binary alloys reported for their high activity in the 2e‐ORR. This analysis also enables the identification of nine selective 2e‐ORR catalysts underscoring the efficacy of ΔΔG as 2e‐ORR selectivity descriptor. This work highlights the significance of concurrently considering both selectivity and activity trends. This holistic approach is crucial for obtaining a comprehensive understanding in the identification of high‐performance catalyst materials for optimal efficiency in various applications. [ABSTRACT FROM AUTHOR]

The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach for mitigating CO2 emissions and generating value-added products. Consequently, discovery of promising CO2RR catalysts has become a crucial task, and machine learning (ML) has been utilized to accelerate catalyst discovery. However, current ML approaches are limited to exploring narrow chemical spaces and provide only fragmentary catalytic activity, even though CO2RR produces various chemicals. Here, by merging pre-developed ML model and a CO2RR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CO2RR without being limited to a database. Further, this strategy can provide guidance on stoichiometry and morphology of the catalyst to researchers. We predict the activity and selectivity of 465 metallic catalysts toward four expected reaction products. During this process, we discover previously unreported and promising behavior of Cu-Ga and Cu-Pd alloys. These findings are then validated through experimental methods. Conventional ab initio calculations and machine learning provide limited information on catalytic activity and selectivity and often show discrepancy with experimental results. Here, the authors report a high-throughput virtual screening strategy to identify active and selective catalysts, leading to the discovery of Cu-Ga and Cu-Pd catalysts for CO2 electroreduction. [ABSTRACT FROM AUTHOR]

In recent years, the development of appropriate crystal representations for accurate prediction of inorganic crystal properties has been considered as one of the essential tasks to accelerate materials discovery through high-throughput virtual screening (HTVS). However, many of them were developed aiming to predict the properties of the given structures, although property predictions of ground state structures using unrelaxed structures as inputs are much more important in practical HTVS. To tackle this challenge, we develop a chemically inspired convolutional neural network based on convolution block attention modules using the density of states of unrelaxed initial structures (IS-DOS) as inputs. Our model, Electronic Structure Network (ESNet), achieved the highest accuracy for predicting formation energy, proving that IS-DOS is an appropriate input for property prediction and the attention module is capable of properly featurizing DOS signals by capturing the contributions of each spin and orbital state. In addition, we statistically evaluated the stability screening performance of ESNet, measuring the computational cost and capability of materials discovery simultaneously. We found that ESNet outperformed previously reported models and various models with different types of input features and architectures. Indeed, ESNet successfully discovered 926 stable materials from 15 318 unrelaxed structures with 82% reduced computational cost compared to the complete DFT validation. [ABSTRACT FROM AUTHOR]

Single‐atom M‒N‒C catalysts have attracted tremendous attention for their application to electrocatalysis. Nitrogen‐coordinated mononuclear metal moieties (MNx moities) are bio‐inspired active sites that are analogous to various metal‐porphyrin cofactors. Given that the functions of metal‐porphyrin cofactors are highly dependent on the local coordination environments around the mononuclear active site, engineering MNx active sites in heterogeneous M‒N‒C catalysts would provide an additional degree of freedom for boosting their electrocatalytic activity. This work presents a local coordination structure modification of FeN4 moieties via morphological engineering of graphene support. Introducing highly wrinkled structure in graphene matrix induces nonplanar distortion of FeN4 moieties, resulting in the modification of electronic structure of mononuclear Fe. Electrochemical analysis combined with first‐principles calculations reveal that enhanced electrocatalytic lithium polysulfide conversion, especially the Li2S redox step, is attributed to the local structure modified FeN4 active sites, while increased specific surface area also contributes to improved performance at low C‐rates. Owing to the synergistic combination of atomic‐level modified FeN4 active sites and morphological advantage of graphene support, Fe‒N‒C catalysts with wrinkled graphene morphology show superior lithium–sulfur battery performance at both low and high C‐rates (particularly 915.9 mAh g−1 at 5 C) with promising cycling stability. [ABSTRACT FROM AUTHOR]

The reconstructed surface structure of Co‐based spinel oxides serves as the active site for oxygen evolution reaction (OER). However, the structural complexity of spinel oxides and surface dynamics during the OER hinder the understanding of the reconstruction mechanism and electronic structure of the active site. In this study, spinel Co3O4@(CoFeV)3O4 nanocube (CoFeV) is reported, a (001) facet‐defined spinel oxide comprising Co, Fe, and V deposited on the Co3O4 nanocube template to exclude facet‐dependent factors. Introducing highly dissoluble V cations accelerates the reconstruction process to enhance the electrocatalytic activity. CoFeV exhibited enhanced electrocatalytic activity (266 mV at 10 mA cm−2 in 1 M KOH) and durability (maintained stable electrocatalytic activity during a 200 h chronopotentiometry (CP) test at 100 mA cm−2) with significantly enlarged electrochemically active surface area (ECSA). The experimental and theoretical results demonstrated that V dissolution during catalysis induced oxygen vacancies, accelerating the surface reconstruction to highly active oxyhydroxide. Consequently, the anion exchange membrane water electrolyzer (AEMWE) of CoFeV as the anode exhibited a remarkable performance of 6.19 A cm−2 at 2.0 Vcell in 1 M KOH and robust durability for 96 h at a constant current density of 500 mA cm−2. [ABSTRACT FROM AUTHOR]

Precise electrochemical synthesis of commodity chemicals and fuels from CO 2 building blocks provides a promising route to close the anthropogenic carbon cycle, in which renewable but intermittent electricity could be stored within the greenhouse gas molecules. Here, we report state-of-the-art CO 2 -to-HCOOH valorization performance over a multiscale optimized Cu-Bi cathodic architecture, delivering a formate Faradaic efficiency exceeding 95% within an aqueous electrolyzer, a C-basis HCOOH purity above 99.8% within a solid-state electrolyzer operated at 100 mA cm -2 for 200 h and an energy efficiency of 39.2%, as well as a tunable aqueous HCOOH concentration ranging from 2.7 to 92.1 wt%. Via a combined two-dimensional reaction phase diagram and finite element analysis, we highlight the role of local geometries of Cu and Bi in branching the adsorption strength for key intermediates like *COOH and *OCHO for CO 2 reduction, while the crystal orbital Hamiltonian population analysis rationalizes the vital contribution from moderate binding strength of η 2 (O,O)-OCHO on Cu-doped Bi surface in promoting HCOOH electrosynthesis. The findings of this study not only shed light on the tuning knobs for precise CO 2 valorization, but also provide a different research paradigm for advancing the activity and selectivity optimization in a broad range of electrosynthetic systems., Competing Interests: Competing interests statement:The authors declare no competing interest.

Understanding selectivity trends is a crucial hurdle in the developing innovative catalysts for generating hydrogen peroxide through the two-electron oxygen reduction reaction (2e-ORR). The identification of selectivity patterns has been made more accessible through the introduction of a newly developed selectivity descriptor derived from thermodynamics, denoted as ΔΔG introduced in Chem Catal. 2023, 3(3), 100568. To validate the suitability of this parameter as a descriptor for 2e-ORR selectivity, we utilize an extensive library of 155 binary alloys. We validate that ΔΔG reliably depicts the selectivity trends in binary alloys reported for their high activity in the 2e-ORR. This analysis also enables the identification of nine selective 2e-ORR catalysts underscoring the efficacy of ΔΔG as 2e-ORR selectivity descriptor. This work highlights the significance of concurrently considering both selectivity and activity trends. This holistic approach is crucial for obtaining a comprehensive understanding in the identification of high-performance catalyst materials for optimal efficiency in various applications., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Se-based nanoalloys as an emerging class of metal chalcogenide with tunable crystalline structure, component distribution, and electronic structure have attracted considerable interest in renewable energy conversion and utilization. In this Letter, we report a series of nanosized M-Se catalysts (M = Cu, Ni, Co) as prepared from laser ablation method and screen their electrocatalytic performance for onsite H 2 O 2 generation from selective oxygen reduction reaction (ORR) in alkaline media. A flexible control on 2e - /4e - ORR pathway has been achieved by engineering the alloying component. Moreover, through a feedback loop between theory and experiment an optimized scaling relationship between oxygenated ORR intermediates has been discovered on cubic Cu 7.2 Se 4 nanocrystals, that is, the ensemble effect of isolated Cu component destabilizes O* binding while the ligand effect of Se to Cu fine-tunes the binding strength of OOH*, leading to a superb H 2 O 2 selectivity above 90% over a wide potential window even after 1400 potential cycles.

To discover new catalysts using density functional theory (DFT) calculations, binding energies of reaction intermediates are considered as descriptors to predict catalytic activities. Recently, machine learning methods have been developed to reduce the number of computationally intensive DFT calculations for a high-throughput screening. These methods require several steps such as bulk structure optimization, surface structure modeling, and active site identification, which could be time-consuming as the number of new candidate materials increases. To bypass these processes, in this work, we report an atomic structure-free representation of active motifs to predict binding energies. We identify binding site atoms and their nearest neighboring atoms positioned in the same layer and the sublayer, and their atomic properties are collected to construct fingerprints. Our method enabled a quicker training (200-400 s using CPU) compared to the previous deep-learning models and predicted CO and H binding energies with mean absolute errors (MAEs) of 0.120 and 0.105 eV, respectively. Our method is also capable of creating all possible active motifs without any DFT calculations and predicting their binding energies using the trained model. The predicted binding energy distributions can suggest promising candidates to accelerate catalyst discovery.