高等学校化学学报

高等学校化学学报

• 研究论文 • 上一篇    下一篇

构建可用于描述固氮催化剂电子供体能力的描述符：机器学习预测吸附能和极限电位

赵迎1, 杨海迪1,2, 柴玉春1,2, 高帅帅1, 原鹏飞1, 陈雪波1,3   

  1. 1.烟台先进材料与绿色制造山东省实验室

    2.哈尔滨工程大学烟台研究院 3.北京师范大学化学学院

  • 收稿日期:2025-09-17 修回日期:2025-10-14 网络首发:2025-10-31 发布日期:2025-10-31
  • 通讯作者: 赵迎 E-mail:yingz@amgm.ac.cn
  • 基金资助:
    国家自然科学基金(批准号：No.22503078)、山东省重点研发计划-竞争性创新平台(批准号：No.2024CXPT036)和山东省泰山学者工程(批准号No.tstp20240844)资助

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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摘要： 在本研究中，通过高通量密度泛函计算筛选出一系列具有NRR活性的B和双金属原子组成的CN-B@M2催化剂。CN-B@Fe2、CN-B@Tc2、CN-B@Os2和CN-B@Re2被认为是具有良好选择性和NRR活性的催化剂，其极限电位(UL)分别为–0.24 eV、–0.34 eV、–0.31 eV和–0.38 eV。计算结果表明，N2在B@M2上的吸附呈现出周期性演变，吸附构型和能量受D带中心调节。UL随转移电荷呈火山型分布。具有中等电子给体能力(中等电荷转移)的B@M2催化剂表现出优异的NRR活性。通过量化催化剂的原子电子特性和拓扑结构，构建了用于描述给电子能力的描述符?。结果表明了给电子能力与氮还原反应的极限电位呈现火山关系。进一步地，使用描述符?和催化剂的内在特性作为特征来预测了吸附能和极限电位，由于R2值为 0.99，梯度提升回归(GBR)被认为是构建机器学习预测模型的最恰当方法。这项工作为合理且高效的氮还原反应催化剂的设计以及其描述符的构建提供了新颖的见解。

关键词: 氮还原, 高通量计算, 机器学习, 供电子能力描述符

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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