TY  - EJOUR
AU  - Rittig, Jan G.
AU  - Ritzert, Martin
AU  - Schweidtmann, Artur M.
AU  - Winkler, Stefanie
AU  - Weber, Jana M.
AU  - Morsch, Philipp
AU  - Heufer, K. Alexander
AU  - Grohe, Martin
AU  - Mitsos, Alexander
AU  - Dahmen, Manuel
TI  - Graph Machine Learning for Design of High-Octane Fuels
PB  - arXiv
M1  - FZJ-2023-00757
PY  - 2022
AB  - 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.
KW  - Machine Learning (cs.LG) (Other)
KW  - FOS: Computer and information sciences (Other)
LB  - PUB:(DE-HGF)25
DO  - DOI:10.48550/ARXIV.2206.00619
UR  - https://juser.fz-juelich.de/record/917555
ER  -