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| 001 | 917555 | ||
| 005 | 20240712112854.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2206.00619 |2 doi |
| 024 | 7 | _ | |a 2128/33645 |2 Handle |
| 037 | _ | _ | |a FZJ-2023-00757 |
| 100 | 1 | _ | |a Rittig, Jan G. |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Graph Machine Learning for Design of High-Octane Fuels |
| 260 | _ | _ | |c 2022 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1673944318_27886 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a 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. |
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| 650 | _ | 7 | |a Machine Learning (cs.LG) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Ritzert, Martin |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Schweidtmann, Artur M. |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Winkler, Stefanie |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Weber, Jana M. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Morsch, Philipp |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Heufer, K. Alexander |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Grohe, Martin |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Mitsos, Alexander |0 P:(DE-Juel1)172025 |b 8 |u fzj |
| 700 | 1 | _ | |a Dahmen, Manuel |0 P:(DE-Juel1)172097 |b 9 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2206.00619 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/917555/files/2206.00619.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2022 |
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