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Electrochemical processes for ammonia synthesis could potentially replace the high temperature and pressure conditions of the Haber-Bosch process, with voltage offering a pathway to distributed fertilizer production that leverages the rapidly decreasing cost of renewable electricity. However, nitrogen is an unreactive molecule and the hydrogen evolution reaction presents a major selectivity challenge. An electrode of electrodeposited lithium in tetrahydrofuran solvent overcomes both problems by providing a surface that easily reacts with nitrogen and by limiting the access of protons with a nonaqueous electrolyte. Under these conditions, we measure relatively high faradaic efficiencies (ca. 10 %) and rates (0.1 mA cm À 2 ) toward NH 3 . We observe the development of a solid electrolyte interface layer as well as the accumulation of lithium and lithium-containing species. Detailed DFT studies suggest lithium nitride and hydride to be catalytically active phases given their thermodynamic and kinetic stability relative to metallic lithium under reaction conditions and the fast diffusion of nitrogen in lithium.[a] Dr.

The active and selective electroreduction of atmospheric nitrogen (N2) to ammonia (NH3) using energy from solar or wind sources at the point of use would enable a sustainable alternative to the Haber–Bosch process for fertilizer production. While the process is thermodynamically possible, experimental attempts thus far have required large overpotentials and have produced primarily hydrogen (H2). In this Perspective, we show how insights from electronic structure calculations of the energetics of the process, combined with mean-field microkinetic modeling, can be used to (1) understand the activity and selectivity challenges in electrochemical NH3 synthesis and (2) propose alternative strategies toward an economically viable process. In particular, we develop the theoretical understanding for two promising actionable avenues that are gaining interest in the experimental literature, (1) circumventing the scaling relations between adsorbed surface intermediates and (2) using nonaqueous electrolytes to suppress the competing hydrogen evolution reaction.

Benchmarking metrics for materials discovery via sequential learning are presented, to assess the efficacy of existing algorithms and to be scientific in our assessment of accelerated science.

The Haber–Bosch process for the reduction of atmospheric nitrogen to ammonia is one of the most optimized heterogeneous catalytic reactions, but there are aspects of the industrial process that remain less than ideal. It has been shown that the activity of metal catalysts is limited by a Brønsted–Evans–Polanyi (BEP) scaling relationship between the reaction and transition-state energies for N2 dissociation, leading to a negligible production rate at ambient conditions and a modest rate under harsh conditions. In this study, we use density functional theory (DFT) calculations in conjunction with mean-field microkinetic modeling to study the rate of NH3 synthesis on model active sites that require the singly coordinated dissociative adsorption of N atoms onto transition metal atoms. Our results demonstrate that this ”on-top” binding of nitrogen exhibits significantly improved scaling behavior, which can be rationalized in terms of transition-state geometries and leads to considerably higher predicted activity. While synthesis of these model systems is likely challenging, the stabilization of such an active site could enable thermochemical ammonia synthesis under more benign conditions.

X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure of materials, but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure. Recently, researchers have demonstrated that machine learning models can automate this process to predict the coordinating environments of absorbing atoms from their XAS spectra. However, machine learning models are often difficult to interpret, making it challenging to determine when they are valid and whether they are consistent with physical theories. In this work, we present three main advances to the data-driven analysis of XAS spectra: we demonstrate the efficacy of random forests in solving two new property determination tasks (predicting Bader charge and mean nearest neighbor distance), we address how choices in data representation affect model interpretability and accuracy, and we show that multiscale featurization can elucidate the regions and trends in spectra that encode various local properties. The multiscale featurization transforms the spectrum into a vector of polynomial-fit features, and is contrasted with the commonly-used “pointwise” featurization that directly uses the entire spectrum as input. We find that across thousands of transition metal oxide spectra, the relative importance of features describing the curvature of the spectrum can be localized to individual energy ranges, and we can separate the importance of constant, linear, quadratic, and cubic trends, as well as the white line energy. This work has the potential to assist rigorous theoretical interpretations, expedite experimental data collection, and automate analysis of XAS spectra, thus accelerating the discovery of new functional materials.

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