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Developing sustainable aviation fuel from C1 feedstocks requires advanced catalysts. Artificial intelligence identified a new design rule for catalysts, enabling efficient conversion to jet fuel hydrocarbons.

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Area of Science:

  • Catalysis
  • Materials Science
  • Artificial Intelligence

Background:

  • Decarbonizing aviation necessitates sustainable drop-in fuels for net-zero targets.
  • Producing energy-dense jet fuels from C1 feedstocks requires complex catalysts beyond simple design rules.

Purpose of the Study:

  • To develop an AI framework for discovering multifunctional catalysts for sustainable aviation fuel (SAF) production.
  • To establish interpretable design principles for catalyst optimization in syngas conversion.

Main Methods:

  • Implemented a dual-engine AI framework combining active learning and interpretable machine learning.
  • Autonomously explored catalyst design spaces for syngas conversion to SAF.
  • Identified specific d-block metal and lanthanide placements on spinel backbones.

Main Results:

  • Discovered novel catalyst compositions, including Zn-Ce/Sm, Fe-Pr/La, and Ni-Ce.
  • Established a general design rule involving d-f interactions and π-back-donation for enhanced adsorption and lower formation barriers.
  • Achieved >75% selectivity to jet-fuel-range aromatic hydrocarbons with high space-time yields.

Conclusions:

  • The AI framework accelerates catalyst discovery and provides transparent, validated design rules.
  • The identified d-f cooperative effect is crucial for efficient C-C coupling in SAF synthesis.
  • This approach offers a generalizable blueprint for interpretable, AI-driven catalyst design in sustainable chemistry.