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Graph machine learning for design of high-octane fuels
Authors:Jan G. Rittig  Martin Ritzert  Artur M. Schweidtmann  Stefanie Winkler  Jana M. Weber  Philipp Morsch  Karl Alexander Heufer  Martin Grohe  Alexander Mitsos  Manuel Dahmen
Affiliation:1. Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany;2. Department of Computer Science, Aarhus University, Aarhus, Denmark;3. Department of Chemical Engineering, Delft University of Technology, Delft, The Netherlands;4. Chair of Computer Science 7, RWTH Aachen University, Aachen, Germany

Contribution: Data curation (supporting), Formal analysis (supporting), Methodology (equal), Software (equal), Validation (supporting), Writing - review & editing (supporting);5. Delft Bioinformatics Lab, Intelligent Systems, Delft University of Technology, Delft, The Netherlands;6. Chair of High Pressure Gas Dynamics, RWTH Aachen University, Aachen, Germany;7. Chair of Computer Science 7, RWTH Aachen University, Aachen, Germany;8. Forschungszentrum Jülich GmbH, Institute for Energy and Climate Research IEK-10: Energy Systems Engineering, Jülich, Germany

Abstract: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.
Keywords:computer-aided molecular design  fuel design  graph machine learning  graph neural networks  machine learning  optimization  renewable fuels  spark-ignition engines
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