Mutual modulation via charge transfer and unpaired electrons of catalyt-ic site for superior intrinsic activity of N2 reduction: from high-throughput computations assisted with machine learning perspective

Abstract

Electrocatalysts of nitrogen reduction reaction (NRR) have attracted ever-growing attention due to its application for renewable energy alternative to fossil fuels. However, activation of inert N-N bond requires multiple complex charge injection which complicates the design of the catalysts. Here via combining atomic-scale screening and machine learning (ML) methods we explore the rational design of NRR single-atom catalysts (SACs) supported by molybdenum disulfide (MoS2). Our work reveals that the activity of NRR SACs is highly dependent on the number of unpaired d electrons of TM: positive samples with high activity favoring higher values while negative cases distributing at lower values, both varying with the doping conditions of the host. We find that the substitution of sulfur with boron can activate the intrinsic NRR activity of some TMs such as Ti and V which are otherwise inactive above pristine MoS2. Importantly, among the various de-scriptors used in ML, the charged state of adsorbed TM plays a key role in donating electron to pi-anti-bonding orbital of N2 via the back-donation mechanism. Our work shows a feasible strategy for rational design of NRR SACs and retrieval of the decisive feature of active catalysts.

References in corpus (3)

• Machine Learning and Evolutionary Prediction of Superhard B-C-N Compounds
• Activation of Phosphorene-like Two-dimensional GeSe for Efficient Electrocatalytic Nitrogen Reduction via States Filtering of Ru
• Substitutional Doped GeSe: Tunable Oxidative States with Strain Engineering

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