000917555 001__ 917555
000917555 005__ 20240712112854.0
000917555 0247_ $$2doi$$a10.48550/ARXIV.2206.00619
000917555 0247_ $$2Handle$$a2128/33645
000917555 037__ $$aFZJ-2023-00757
000917555 1001_ $$0P:(DE-HGF)0$$aRittig, Jan G.$$b0
000917555 245__ $$aGraph Machine Learning for Design of High-Octane Fuels
000917555 260__ $$barXiv$$c2022
000917555 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1673944318_27886
000917555 3367_ $$2ORCID$$aWORKING_PAPER
000917555 3367_ $$028$$2EndNote$$aElectronic Article
000917555 3367_ $$2DRIVER$$apreprint
000917555 3367_ $$2BibTeX$$aARTICLE
000917555 3367_ $$2DataCite$$aOutput Types/Working Paper
000917555 520__ $$aFuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further auto-ignition training data.
000917555 536__ $$0G:(DE-HGF)POF4-1121$$a1121 - Digitalization and Systems Technology for Flexibility Solutions (POF4-112)$$cPOF4-112$$fPOF IV$$x0
000917555 588__ $$aDataset connected to DataCite
000917555 650_7 $$2Other$$aMachine Learning (cs.LG)
000917555 650_7 $$2Other$$aFOS: Computer and information sciences
000917555 7001_ $$0P:(DE-HGF)0$$aRitzert, Martin$$b1
000917555 7001_ $$0P:(DE-HGF)0$$aSchweidtmann, Artur M.$$b2
000917555 7001_ $$0P:(DE-HGF)0$$aWinkler, Stefanie$$b3
000917555 7001_ $$0P:(DE-HGF)0$$aWeber, Jana M.$$b4
000917555 7001_ $$0P:(DE-HGF)0$$aMorsch, Philipp$$b5
000917555 7001_ $$0P:(DE-HGF)0$$aHeufer, K. Alexander$$b6
000917555 7001_ $$0P:(DE-HGF)0$$aGrohe, Martin$$b7
000917555 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b8$$ufzj
000917555 7001_ $$0P:(DE-Juel1)172097$$aDahmen, Manuel$$b9$$eCorresponding author$$ufzj
000917555 773__ $$a10.48550/ARXIV.2206.00619
000917555 8564_ $$uhttps://juser.fz-juelich.de/record/917555/files/2206.00619.pdf$$yOpenAccess
000917555 909CO $$ooai:juser.fz-juelich.de:917555$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000917555 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b0$$kRWTH
000917555 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b1$$kRWTH
000917555 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b3$$kRWTH
000917555 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b5$$kRWTH
000917555 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b6$$kRWTH
000917555 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b7$$kRWTH
000917555 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172025$$aForschungszentrum Jülich$$b8$$kFZJ
000917555 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)172025$$aRWTH Aachen$$b8$$kRWTH
000917555 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172097$$aForschungszentrum Jülich$$b9$$kFZJ
000917555 9131_ $$0G:(DE-HGF)POF4-112$$1G:(DE-HGF)POF4-110$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1121$$aDE-HGF$$bForschungsbereich Energie$$lEnergiesystemdesign (ESD)$$vDigitalisierung und Systemtechnik$$x0
000917555 9141_ $$y2022
000917555 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000917555 920__ $$lyes
000917555 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
000917555 9801_ $$aFullTexts
000917555 980__ $$apreprint
000917555 980__ $$aVDB
000917555 980__ $$aUNRESTRICTED
000917555 980__ $$aI:(DE-Juel1)IEK-10-20170217
000917555 981__ $$aI:(DE-Juel1)ICE-1-20170217