%0 Electronic Article
%A Rittig, Jan G.
%A Ritzert, Martin
%A Schweidtmann, Artur M.
%A Winkler, Stefanie
%A Weber, Jana M.
%A Morsch, Philipp
%A Heufer, K. Alexander
%A Grohe, Martin
%A Mitsos, Alexander
%A Dahmen, Manuel
%T Graph Machine Learning for Design of High-Octane Fuels
%I arXiv
%M FZJ-2023-00757
%D 2022
%X 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.
%K Machine Learning (cs.LG) (Other)
%K FOS: Computer and information sciences (Other)
%F PUB:(DE-HGF)25
%9 Preprint
%R 10.48550/ARXIV.2206.00619
%U https://juser.fz-juelich.de/record/917555