Graph Machine Learning for Design of High-Octane Fuels

Rittig, Jan G.; Dahmen, Manuel; Heufer, K. Alexander; Morsch, Philipp; Schweidtmann, Artur M.; Weber, Jana M.; Grohe, Martin; Ritzert, Martin; Mitsos, Alexander; Winkler, Stefanie

doi:10.48550/ARXIV.2206.00619

Preprint

FZJ-2023-00757

Graph Machine Learning for Design of High-Octane Fuels

Rittig, J. G.RWTH* ; Ritzert, M.RWTH* ; Schweidtmann, A. M. ; Winkler, S.RWTH* ; Weber, J. M. ; Morsch, P.RWTH* ; Heufer, K. A.RWTH* ; Grohe, M.RWTH* ; Mitsos, A.FZJ*RWTH* ; Dahmen, M. (Corresponding author)FZJ*

2022
arXiv

arXiv (2022) [10.48550/ARXIV.2206.00619]

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Please use a persistent id in citations: http://hdl.handle.net/2128/33645 doi:10.48550/ARXIV.2206.00619

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.

Keyword(s): Machine Learning (cs.LG) ; FOS: Computer and information sciences

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