发表机构
Institut National de la Recherche Scientifique（国家科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究结合高通量DFT计算与机器学习，构建数据集评估47种双金属IMC关键中间体吸附能，开发预测模型。发现p和s带特征对吸附强度影响更显著，挑战d带中心范式，重新定义电子描述符空间，为发现非贵金属氨合成催化剂奠定基础。
AI 中文摘要
本研究结合高通量DFT计算和机器学习技术，以揭示金属间化合物（IMC）中氮还原反应（NRR）的关键描述符。构建了包含47种双金属IMC的数据集，系统评估了所有可及表面位点上关键中间体（N2、N2H和NH3）的吸附能，产生约1200个数据点。通过纳入材料固有属性以及包括s、p和d带中心与填充等电子描述符，以及吸附质邻近原子的Bader电荷，开发了预测性ML模型，N2、N2H和NH3吸附的平均绝对误差分别为0.26 eV、0.39 eV和0.17 eV。重要的是，仅用20个关键特征就能获得准确预测。SHAP分析表明，p和s带特征在决定吸附强度方面比传统的d带中心更突出。通过挑战以d带为中心的范式，确定s和p带描述符为关键但被忽视的贡献者，本工作重新定义了金属间NRR催化剂的电子描述符空间，为DFT-ML指导发现用于可持续氨合成的非贵金属材料奠定了基础。
英文摘要
This study combines high-throughput density functional theory (DFT) calculations with machine learning (ML) to uncover the key descriptors governing the nitrogen reduction reaction in 47 intermetallic compounds (IMCs) composed of Co, Ni, Al, Zn, V, Fe, Cu, and Pt, and the adsorption energies of key intermediates (*N2, *N2H, and *NH3) were systematically evaluated across all accessible surface sites, yielding approximately 1,200 data points. Among the IMCs studied, Fe9Co7 and Fe3Co emerge as the most balanced catalysts, exhibiting favorable adsorption energies across all three intermediates. By incorporating intrinsic material properties along with local and global electronic descriptors including s-, p- and d-band centers and fillings, as well as Bader charges of atoms neighboring the adsorbate, predictive ML models were developed with mean absolute errors (MAEs) of 0.27 eV for *N2, 0.39 eV for *N2H, and 0.17 eV for *NH3 adsorption. Importantly, accurate adsorption-energy predictions were achieved using only 20 key features for *N2H and *NH3 and 38 features for *N2, enabling the use of simple and computationally efficient ML models. SHAP analysis indicates that p-band and s-band characteristics play a more prominent role in determining adsorption strength than the traditionally used d-band center, particularly for *N2. Beyond their established importance in systems containing p-block elements or nearly filled d-band metals, s- and p-orbitals are also found to contribute significantly to transition metal alloy activity such as Fe-Co. By challenging the d-band-centric paradigm and identifying s- and p-band descriptors as critical yet overlooked contributors, this work redefines the electronic descriptor space for intermetallic NRR catalysts and lays the groundwork for DFT-ML-guided discovery of non-noble, compositionally complex materials for sustainable ammonia synthesis.
