Graph Machine Learning for Design of High-Octane Fuels

RWTH Publications (RWTH Aachen)

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 auto-ignition training data.

Authors 9

1. Jan G. Rittig Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

   RWTH Aachen University, Process Systems Engineering (AVT.SVT), Aachen 52074, Germany

2. Martin Ritzert

   Aarhus University

   Affiliation as printed

   Aarhus University, Department of Computer Science, Aarhus, Denmark

3. Artur M. Schweidtmann Aachen

   RWTH Aachen University · Delft University of Technology

   Affiliation as printed

   RWTH Aachen

   Delft University of Technology, Department of Chemical Engineering, Delft 2629 HZ, The Netherlands

4. Jana M. Weber

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft Bioinformatics Lab, Intelligent Systems, 2628 XE Delft, The Netherlands

5. Philipp Morsch Aachen Chair of High Pressure Gas Dynamics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

   RWTH Aachen University, Chair of High Pressure Gas Dynamics, Aachen 52074, Germany

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

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

   RWTH Aachen University, Chair of High Pressure Gas Dynamics, Aachen 52074, Germany

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

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

   RWTH Aachen University, Chair of Computer Science 7, Aachen 52074, Germany

8. Alexander Mitsos Aachen

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

   Forschungszentrum Jülich GmbH, Institute for Energy and Climate Research IEK-10: Energy Systems Engineering, Jülich 52425, Germany

   JARA-ENERGY, Aachen 52056, Germany

   RWTH Aachen University, Process Systems Engineering (AVT.SVT), Aachen 52074, 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 52425, Germany