Machine learning-assisted Ru-N bond regulation for ammonia synthesis.

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

SUBMITTER: Li Z 

PROVIDER: S-EPMC12370904 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature