A Dual-Engine Artificial Intelligence Framework Accelerates Sustainable Aviation Fuel Component Synthesis

Feedstocks demands multifunctional catalysts whose performance arises from nonlinear, high-dimensional interactions─beyond single-descriptor design rules. Here we present a dual-engine artificial intelligence framework that couples closed-loop active learning with interpretable machine learning, demonstrated for syngas conversion to sustainable aviation fuel. The approach autonomously explores vast catalyst spaces while distilling human-interpretable principles. We identify previously unreported compositions and a general rule: on a stable spinel backbone, placing a d-block metal at the tetrahedral (A)-site and an early lanthanide at the octahedral (B)-site creates cooperative d-f interactions that enable…

• NN
  National Natural Science Foundation of China

  Award: 22278238
• CP
  China Postdoctoral Science Foundation

  Award: 2025M781124
• CN
  China National Petroleum Corporation

  Award: 2020990028
• NS
  Natural Science Foundation of Sichuan Province

  Award: 2026NSFSC0045