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  1. Abstract

    A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble. The approach relies upon a dual-learning scheme, where both the system charge and the system energy are predicted for each image. The scheme is shown to be capable of emulating basic electrochemical reactions at a range of potentials, and coupling it with a bootstrap-ensemble approach gives reasonable estimates of the prediction uncertainty. The method is also demonstrated to accelerate saddle-point searches, and to extrapolate to systems with one to five water layers. We anticipate that this method will allow for larger length- and time-scale simulations necessary for electrochemical simulations.

     
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  2. Abstract

    Photocatalytic nitrogen fixation has the potential to provide a greener route for producing nitrogen‐based fertilizers under ambient conditions. Computational screening is a promising route to discover new materials for the nitrogen fixation process, but requires identifying “descriptors” that can be efficiently computed. In this work, we argue that selectivity toward the adsorption of molecular nitrogen and oxygen can act as a key descriptor. A catalyst that can selectively adsorb nitrogen and resist poisoning of oxygen and other molecules present in air has the potential to facilitate the nitrogen fixation process under ambient conditions. We provide a framework for active site screening based on multifidelity density functional theory (DFT) calculations for a range of metal oxides, oxyborides, and oxyphosphides. The screening methodology consists of initial low‐fidelity fixed geometry calculations and a second screening in which more expensive geometry optimizations were performed. The approach identifies promising active sites on several TiO2polymorph surfaces and a VBO4surface, and the full nitrogen reduction pathway is studied with the BEEF‐vdW and HSE06 functionals on two active sites. The findings suggest that metastable TiO2polymorphs may play a role in photocatalytic nitrogen fixation, and that VBO4may be an interesting material for further studies.

     
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  3. Abstract

    Electrochemical nitrate reduction reaction (NO3RR) has garnered increasing attention as a pathway for converting a harmful pollutant (nitrate) into a value‐added product (ammonia). However, high selectivity toward ammonia (NH3) is imperative for process viability. Optimizing proton availability near the catalyst is important for achieving selective NH3production. Here, the aim is to systematically examine the impacts of proton availability on NO3RR selectivity in a bipolar membrane (BPM)‐based membrane electrode assembly (MEA) system. The BPM generates a proton flux from the membrane toward the catalyst during electrolysis. Thus, the BPM‐MEA system can modulate the proton flux during operation. The impact of interposer layers, proton scavenging electrolytes (CO32−), and catalyst configurations are also examined to identify which local microenvironments favor ammonia formation. It is found that a moderate proton supply allows for an increase in ammonia yield by 576% when compared to the standard MEA setup. This also results in a high selectivity of 26 (NH3over NO2) at an applied current density of 200 mA cm−2.

     
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  4. null (Ed.)