Print Email Facebook Twitter Graph machine learning for design of high-octane fuels Title Graph machine learning for design of high-octane fuels Author Rittig, J. (Rheinisch-Westfälische Technische Hochschule) Ritzert, Martin (Aarhus University) Schweidtmann, A.M. (TU Delft ChemE/Product and Process Engineering) Winkler, Stefanie (Rheinisch-Westfälische Technische Hochschule) Weber, J.M. (TU Delft Pattern Recognition and Bioinformatics) Morsch, Philipp (Rheinisch-Westfälische Technische Hochschule) Heufer, Karl Alexander (Rheinisch-Westfälische Technische Hochschule) Grohe, Martin (Rheinisch-Westfälische Technische Hochschule) Mitsos, Alexander (Rheinisch-Westfälische Technische Hochschule; Forschungszentrum Jülich) Dahmen, Manuel (Forschungszentrum Jülich) Date 2022 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 autoignition training data. Subject computer-aided molecular designfuel designgraph machine learninggraph neural networksmachine learningoptimizationrenewable fuelsspark-ignition engines To reference this document use: http://resolver.tudelft.nl/uuid:e645b25f-716a-4fa2-8b91-6803ac781d0f DOI https://doi.org/10.1002/aic.17971 ISSN 0001-1541 Source AIChE Journal, 69 (4) Part of collection Institutional Repository Document type journal article Rights © 2022 J. Rittig, Martin Ritzert, A.M. Schweidtmann, Stefanie Winkler, J.M. Weber, Philipp Morsch, Karl Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen Files PDF AIChE_Journal_2022_Rittig ... _fuels.pdf 2.5 MB Close viewer /islandora/object/uuid:e645b25f-716a-4fa2-8b91-6803ac781d0f/datastream/OBJ/view