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Construction of descriptors that can be used to describe electron donating ability of nitrogen fixing catalysts: machine learning predict adsorption energies and limiting potentials

ZHAO Ying1, YANG Haidi1,2, CHAI Yuchun1,2, GAO Shuaishuai1, YUAN Pengfei1, CHEN Xuebo1,3   

  1. 1. Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai

    2. Yantai Research Institute, Harbin Engineering University 3. College of Chemistry, Beijing Normal University

  • Received:2025-09-17 Revised:2025-10-14 Online First:2025-10-31 Published:2025-10-31
  • Contact: Ying ZHAO E-mail:yingz@amgm.ac.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China(No.22503078), the Key Research and Development Program of Shandong Province, China(No.2024CXPT036) and the Program of Taishan Scholars of Shandong Province, China(No.tstp20240844)

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Abstract: In this work, a series of CN-B@M2 catalysts composed of B and bimetallic atoms with NRR activity are screened by high-throughput density functional calculations. CN-B@Fe2, CN-B@Tc2, CN-B@Os2, and CN-B@Re2 are considered as catalysts with good selectivity and NRR activity, with the limiting potentials of -0.24 eV, -0.34 eV, -0.31eV, and -0.38eV, respectively. Calculation results show that the adsorption configuration of N2 at B@M2 shows a periodic evolution, and adsorption configuration and energy are regulated by D-band center. UL shows a volcanic distribution with adsorbed N2 charge. B@M2 catalyst with moderate electron donor capacity showed excellent NRR activity. Descriptor ? used to describe electron donating ability is constructed by quantifying atom electronic properties and topology structure of catalysts. ? shows a strong linear correlation with adsorption energy, and describes limiting potential of NRR by volcano diagram. ? and intrinsic properties of catalyst are used as features to predict the adsorption energy and UL. GBR is considered the most appropriate method for building a machine learning prediction model due to an R2 of 0.99. This work provides novel insights into the design of rational and efficient NRR catalysts and construction of their descriptors.

Key words:

Nitrogen reduction; High-throughput computing, Machine learning, Descriptor of supply electrons ability

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