Machine learning-assisted Ru-N bond regulation for ammonia synthesis
Zichuang Li, Mingxin Zhang, Xiaozhi Su, Yangfan Lu, Jiang Li, Qing Zhang, Wenqian Li, Kailong Qian, Xiaojun Lu, Bo Dai, Hideo Hosono, Yanpeng Qi, Miao Xu, Renzhong Tai, Jie‐Sheng Chen, Tian‐Nan Ye
Abstract
Ruthenium-bearing intermetallics (Ru-IMCs) featured with well-defined structures and variable compositions offer new opportunities to develop ammonia synthesis catalysts under mild conditions. However, their complex phase nature and the numerous controlling parameters pose major challenges for catalyst design and exploration. Herein, we demonstrate that a combination of machine learning (ML) and model mining techniques can effectively address these challenges. Based on the combination techniques, we generate a two-dimensional activity volcano plot with adsorption energies of N2 and N, and identify the radius of atom coordinating to Ru as a key parameter. The well-designed Sc1/8Nd7/8Ru2 reaches as high as 8.18 mmol m−2 h−1 at 0.1 MPa and 400 °C. Density functional theory (DFT) calculations combined with N2-TPD and FT-IR studies reveal that Ru‒N interaction is controlled by the d-p orbital hybridization between Ru and N. These findings underscore the importance of ML towards material design on exploring IMCs for ammonia synthesis. Developing Ru-intermetallic catalysts for mild ammonia synthesis faces structural complexity. Here, machine learning identified Sc1/8Nd7/8Ru2, optimizing Ru–N bonding and orbital hybridization, enhancing catalytic activity under mild conditions.