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| Hauptseite > Publikationsdatenbank > Graph Machine Learning for Design of High-Octane Fuels |
| Hauptseite > Publikationsdatenbank > Graph Machine Learning for Design of High-Octane Fuels |
| Preprint | FZJ-2023-00757 |
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2022
arXiv
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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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