This randomized trial investigates how different crystal facets affect ammonia decomposition efficiency in transition metals, suggesting ways to improve catalytic performance.
Transition-metal nanoparticles have been extensively investigated as catalysts for ammonia decomposition, and the crystal facet effect of these metals has not yet been comprehensively elucidated. Here, the catalytic performance of ammonia decomposition on low-index crystal facets of Fe, Co, Ni, Cu, Ru, Rh, Ir, and Pt was systematically investigated by employing density functional theory (DFT) and machine learning (ML). All investigated crystal facets effectively activate NH 3 molecules, with further regulation of nitrogen atom desorption achievable by altering the exposed metal crystal facet. Among the 24 studied crystal facets, Ru(110), Co(001), and Fe(100) exhibit outstanding NH 3 dissociation activity, whereas Fe(100) demonstrates limited catalytic efficiency due to an excessively high N 2 binding energy barrier. Comparative analysis indicates that Ru(110) and Co(001) possess optimal catalytic performance, with reaction energy barriers of 1.12 and 1.18 eV, respectively. The projected density of states (PDOS) and integrated crystal orbital Hamilton population (ICOHP) calculations of the N–H bond in the rate-determining step for Ru(110) and Co(001) reveal that stronger N-M interactions correlate with enhanced NH 3 adsorption on Ru(110). This further facilitates the activation of the N–H bond in the rate-determining step, thereby granting Ru(110) superior catalytic activity compared with Co(001). The charge density difference and Bader charge analysis reveal that the metal–nitrogen bond acts as an electron transfer bridge, facilitating electron migration from the NH 3 molecule to the substrate and inducing charge redistribution. This effect enhances the chemical adsorption of NH 3 and improves the activation efficiency. Furthermore, an expression relating geometric structure, electronic structure, and ammonia activation was established by sure independence screening and sparsifying operator (SISSO). The interdisciplinary approach of DFT-ML provides insights into the origin of NH 3 adsorption and offers a novel pathway for designing high-performance catalysts.
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Deng et al. (2026) studied this question.
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