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.
In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are ...made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity.
Graphical Abstract
The electrochemical synthesis of ammonia from nitrogen under mild conditions using renewable electricity is an attractive alternative
to the energy-intensive Haber-Bosch process, which dominates ...industrial ammonia production. However, there are considerable scientific and technical challenges
facing the electrochemical alternative, and most experimental studies reported so far have achieved only low selectivities and conversions. The amount of ammonia produced is usually so small that it cannot be firmly attributed to electrochemical nitrogen fixation
rather than contamination from ammonia that is either present in air, human breath or ion-conducting membranes
, or generated from labile nitrogen-containing compounds (for example, nitrates, amines, nitrites and nitrogen oxides) that are typically present in the nitrogen gas stream
, in the atmosphere or even in the catalyst itself. Although these sources of experimental artefacts are beginning to be recognized and managed
, concerted efforts to develop effective electrochemical nitrogen reduction processes would benefit from benchmarking protocols for the reaction and from a standardized set of control experiments designed to identify and then eliminate or quantify the sources of contamination. Here we propose a rigorous procedure using
N
that enables us to reliably detect and quantify the electrochemical reduction of nitrogen to ammonia. We demonstrate experimentally the importance of various sources of contamination, and show how to remove labile nitrogen-containing compounds from the nitrogen gas as well as how to perform quantitative isotope measurements with cycling of
N
gas to reduce both contamination and the cost of isotope measurements. Following this protocol, we find that no ammonia is produced when using the most promising pure-metal catalysts for this reaction in aqueous media, and we successfully confirm and quantify ammonia synthesis using lithium electrodeposition in tetrahydrofuran
. The use of this rigorous protocol should help to prevent false positives from appearing in the literature, thus enabling the field to focus on viable pathways towards the practical electrochemical reduction of nitrogen to ammonia.
Sequential learning (SL) strategies,
i.e.
iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. ...Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any "good" material, discovery of all "good" materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery.
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.
Ammonia synthesis is one of the most studied reactions in heterogeneous catalysis. To date, however, electrochemical N
reduction in aqueous systems has proven to be extremely difficult, mainly due to ...the competing hydrogen evolution reaction (HER). Recently, it has been shown that transition metal complexes based on molybdenum can reduce N
to ammonia at room temperature and ambient pressure in a non-aqueous system, with a relatively small amount of hydrogen output. We demonstrate that the non-aqueous proton donor they have chosen, 2,6-lutidinium (LutH
), is a viable substitute for hydronium in the electrochemical process at a solid surface, since this donor can suppress the HER rate. We also show that the presence of LutH
can selectively stabilize the *NNH intermediate relative to *NH or *NH
via the formation of hydrogen bonds, indicating that the use of non-aqueous solvents can break the scaling relationship between limiting potential and binding energies.
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•Density functional theory suggests new structure for ammonia catalyst active site.•K-promoter stabilizes the N-N transition state and destabilizes late intermediates.•20 ppm water ...vapor is detrimental to ammonia production rate at low temperature.•Weak-binding catalysts are less susceptible to oxygen poisoning.•Mitigating oxygen poisoning is essential to low-temperature ammonia synthesis.
The Haber-Bosch process has been studied extensively, yet a low-temperature, low-pressure process remains elusive. As has been shown many times, this stems in part from the difficulty of breaking the N-N triple bond. In this work, we highlight an additional reason for the lack of a low-temperature ammonia synthesis process: the effect of oxygen poisoning at low temperature. Using density functional theory (DFT), we have created a new model for the active site of industrial Haber-Bosch catalysts which explicitly includes the potassium promoter. Furthermore, we present a new micro-kinetic model for ammonia synthesis that includes the effect of oxygen poisoning due to trace water content in the input gas stream. Our model agrees well with previous experiments and shows that devising a strategy to avoid oxygen poisoning is crucial to creating a low-temperature Haber-Bosch process. Additionally, the model suggests that using a weaker-binding catalyst is one way to avoid oxygen poisoning if it is impractical to remove all water from the reactor.
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.
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.
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−2) toward NH3. 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 dynamic trio: Electrodeposited lithium is an active electrochemical ammonia synthesis catalyst. Voltammetry and impedance spectroscopy show that the active surface is composed of reduced lithium species. Density functional theory shows the activity role for lithium metal, lithium nitride, and lithium hydride.
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