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000917618 1001_ $$0P:(DE-HGF)0$$aRittig, Jan G.$$b0
000917618 245__ $$aGraph machine learning for design of high‐octane fuels
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000917618 520__ $$aFuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.
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000917618 7001_ $$0P:(DE-HGF)0$$aRitzert, Martin$$b1
000917618 7001_ $$0P:(DE-HGF)0$$aSchweidtmann, Artur M.$$b2
000917618 7001_ $$0P:(DE-HGF)0$$aWinkler, Stefanie$$b3
000917618 7001_ $$0P:(DE-HGF)0$$aWeber, Jana M.$$b4
000917618 7001_ $$0P:(DE-HGF)0$$aMorsch, Philipp$$b5
000917618 7001_ $$0P:(DE-HGF)0$$aHeufer, Karl Alexander$$b6
000917618 7001_ $$0P:(DE-HGF)0$$aGrohe, Martin$$b7
000917618 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b8$$ufzj
000917618 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b9$$eCorresponding author$$ufzj
000917618 773__ $$0PERI:(DE-600)2020333-0$$a10.1002/aic.17971$$n4$$pe17971$$tAIChE journal$$v69$$x0001-1541$$y2023
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