%0 Journal Article
%A Rittig, Jan G.
%A Ritzert, Martin
%A Schweidtmann, Artur M.
%A Winkler, Stefanie
%A Weber, Jana M.
%A Morsch, Philipp
%A Heufer, Karl Alexander
%A Grohe, Martin
%A Mitsos, Alexander
%A Dahmen, Manuel
%T Graph machine learning for design of high‐octane fuels
%J AIChE journal
%V 69
%N 4
%@ 0001-1541
%C Hoboken, NJ
%I Wiley
%M FZJ-2023-00813
%P e17971
%D 2023
%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 autoignition training data.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000903130000001
%R 10.1002/aic.17971
%U https://juser.fz-juelich.de/record/917618