Graph Machine Learning for Design of High-Octane Fuels
| Autor: | Rittig, Jan G., Ritzert, Martin, Schweidtmann, Artur M., Winkler, Stefanie, Weber, Jana M., Morsch, Philipp, Heufer, K. Alexander, Grohe, Martin, Mitsos, Alexander, Dahmen, Manuel |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | AIChE Journal 69 (4), e17971, 2023 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1002/aic.17971 |
| Popis: | 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. Comment: manuscript (26 pages, 9 figures, 2 tables), supporting information (12 pages, 8 figures, 1 table) |
| Databáze: | arXiv |
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