  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.
