Graph machine learning for design of high-octane fuels
J. Rittig (RWTH Aachen University)
Martin Ritzert (Aarhus University)
A.M. Schweidtmann (TU Delft - ChemE/Product and Process Engineering)
Stefanie Winkler (RWTH Aachen University)
J.M. Weber (TU Delft - Pattern Recognition and Bioinformatics)
Philipp Morsch (RWTH Aachen University)
Karl Alexander Heufer (RWTH Aachen University)
Martin Grohe (RWTH Aachen University)
Alexander Mitsos (Forschungszentrum Jülich, RWTH Aachen University)
Manuel Dahmen (Forschungszentrum Jülich)
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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.
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