Graph machine learning for design of high‐octane fuels

AIChE Journal, vol. 69

Abstract

Abstract Fuels with high‐knock resistance enable modern spark‐ignition engines to achieve high efficiency and thus low CO 2 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.

Authors 9

1. Jan G. Rittig Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT) RWTH Aachen University Aachen Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

2. Martin Ritzert

   Aarhus University

   Affiliation as printed

   Department of Computer Science Aarhus University Aarhus Denmark

   Department of Computer Science, Aarhus University, Aarhus, Denmark

3. Artur M. Schweidtmann

   Delft University of Technology

   Affiliation as printed

   Department of Chemical Engineering Delft University of Technology Delft The Netherlands

   Department of Chemical Engineering, Delft University of Technology, Delft, The Netherlands

4. Jana M. Weber

   Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Intelligent Systems Delft University of Technology Delft The Netherlands

   Delft Bioinformatics Lab, Intelligent Systems, Delft University of Technology, Delft, The Netherlands

5. Philipp Morsch Aachen Chair of High Pressure Gas Dynamics

   RWTH Aachen University

   Affiliation as printed

   Chair of High Pressure Gas Dynamics RWTH Aachen University Aachen Germany

   Chair of High Pressure Gas Dynamics, RWTH Aachen University, Aachen, Germany

6. Karl Alexander Heufer Aachen Chair of High Pressure Gas Dynamics

   RWTH Aachen University

   Affiliation as printed

   Chair of High Pressure Gas Dynamics RWTH Aachen University Aachen Germany

   Chair of High Pressure Gas Dynamics, RWTH Aachen University, Aachen, Germany

7. Martin Grohe Aachen Chair of Computer Science 7 (Logic and Theory of Discrete Systems)

   RWTH Aachen University

   Affiliation as printed

   Chair of Computer Science 7 RWTH Aachen University Aachen Germany

   Chair of Computer Science 7, RWTH Aachen University, Aachen, Germany

8. Alexander Mitsos Aachen Process Systems Engineering (AVT.SVT)

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH Institute for Energy and Climate Research IEK‐10: Energy Systems Engineering Jülich Germany

   JARA‐ENERGY Aachen Germany

   Process Systems Engineering (AVT.SVT) RWTH Aachen University Aachen Germany

   JARA-ENERGY, Aachen, Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

9. Manuel Dahmen corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH Institute for Energy and Climate Research IEK‐10: Energy Systems Engineering Jülich Germany