Graph machine learning for design of high-octane fuels

Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann, Stefanie Winkler, Jana M. Weber, Philipp Morsch, Karl Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.

Original languageEnglish
Number of pages18
JournalAIChE Journal
Publication statusPublished - 2022


  • computer-aided molecular design
  • fuel design
  • graph machine learning
  • graph neural networks
  • machine learning
  • optimization
  • renewable fuels
  • spark-ignition engines


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