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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]

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.