Physically-Based Descriptors of the Nitrogen Reduction Reaction on Transition Metal Nitrides
Authors: Athanitis Constantine , Yildiz Bilge 
Published: August 2024

Due to the high energy intensity and consequent carbon footprint of the Haber-Bosch process for ammonia production, researchers have been intensely studying the electrochemical nitrogen reduction reaction (NRR) as a means to directly produce ammonia using clean energy with lower emissions of carbon by-products. One exciting class of materials for NRR catalysis are transition metal nitrides (TMNs), as they have been shown to have good stability, flexible electronic structures, and decent electrochemical activity [1]. Previous studies using density functional theory (DFT) have explored the energetics of known NRR pathways (associative, dissociative, and Mars-van Krevelen) on the mononitrides of all naturally occurring d-block metals in rock salt and zincblende structures [2]. The most favorable NRR mechanism (determined to be the pathway that contains the smallest maximum potential change across all steps of the reaction) on these materials was shown to be the Mars-van Krevelen mechanism. Uncovering electronic, structural, and dynamic descriptors that correlate well with the energetics of this favorable NRR mechanism on TMNs will be useful in mitigating the need to run full DFT calculations when expanding the search space of potential catalyst candidates, such as ternary transition metal nitrides (TTMNs) and perovskite transition metal oxynitrides (TMONs). Previous literature has shown that the proton adsorption energy is a good descriptor for catalytic NRR activity on TMNs [3], oxygen vacancy content [4] and the oxygen p-band center of bulk perovskites [5] are good descriptors for catalytic oxygen evolution reaction performance, and the metal d-band center of pure transition metal catalysts is a good descriptor for reactivity and adsorption energies [6]. Inspired by these results, this work uses DFT on a set of TMNs to calculate a variety of potential physics-based descriptors, including: metal d and nitrogen p-band centers, hybridization between nitrogen p and metal d orbitals, nitrogen vacancy content, surface Bader charges, and phonon band centers. Since the energetics of the NRR can be modulated by the properties of either the atoms in the bulk or in the top surface layers, we calculate all of our descriptors from atoms in the TMN bulk form as well as from surface and subsurface atoms in the TMN slab form.
Our results reveal several correlations between the descriptors and material attributes (such as adsorption energies and vacancy formation energies), which in turn govern the energetics of the NRR on the TMNs. We find that the N2 adsorption energy is negatively correlated with the metal d-band center, the N adsorption energy is positively correlated with the nitrogen p-band center, and both the proton adsorption energy and nitrogen vacancy formation energy are negatively correlated with nitrogen p and metal d orbital hybridization. Interestingly, we also find that for some attributes, the subsurface layer atoms dictate the attribute values and not the surface layer atoms. This suggests that heterogeneous catalysis reactivity may be modulated by atomic properties not purely from the bulk or purely from the surface, but rather from multiple subsurface layers as well.
Overall, the insights gained from our descriptor analysis can be used to constrain the search and design space of future potential NRR catalysts, such as TTMNs and TMONs. In doing so, the descriptors can be used in future works in either a high-throughput screening or machine learning framework to allow for the inverse material design of optimal NRR catalysts. These optimal catalysts can in turn lead to the emergence of electrochemical ammonia synthesis in industrial production.
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