Search Results - "Rittig, Jan G."

Graph machine learning for design of high‐octane fuels by Rittig, Jan G., Ritzert, Martin, Schweidtmann, Artur M., Winkler, Stefanie, Weber, Jana M., Morsch, Philipp, Heufer, Karl Alexander, Grohe, Martin, Mitsos, Alexander, Dahmen, Manuel

“…Fuels with high‐knock resistance enable modern spark‐ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with…”
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Thermodynamics-consistent graph neural networks by Rittig, Jan G, Mitsos, Alexander

“…We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting composition-dependent activity coefficients of binary mixtures. The GE-GNN…”
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Graph Neural Networks for Prediction of Fuel Ignition Quality by Schweidtmann, Artur M, Rittig, Jan G, König, Andrea, Grohe, Martin, Mitsos, Alexander, Dahmen, Manuel

“…Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure–property…”
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Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks by Brozos, Christoforos, Rittig, Jan G., Bhattacharya, Sandip, Akanny, Elie, Kohlmann, Christina, Mitsos, Alexander

“…The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in the industry. Recently, classical…”
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Gibbs-Duhem-informed neural networks for binary activity coefficient prediction by Rittig, Jan G, Felton, Kobi C, Lapkin, Alexei A, Mitsos, Alexander

“…We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem…”
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Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids by Rittig, Jan G., Ben Hicham, Karim, Schweidtmann, Artur M., Dahmen, Manuel, Mitsos, Alexander

“…Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix…”
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Graph neural networks for surfactant multi-property prediction by Brozos, Christoforos, Rittig, Jan G., Bhattacharya, Sandip, Akanny, Elie, Kohlmann, Christina, Mitsos, Alexander

“…Surfactants are of high importance in different industrial sectors such as cosmetics, detergents, oil recovery and drug delivery systems. Therefore, many…”
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Physical pooling functions in graph neural networks for molecular property prediction by Schweidtmann, Artur M., Rittig, Jan G., Weber, Jana M., Grohe, Martin, Dahmen, Manuel, Leonhard, Kai, Mitsos, Alexander

“…Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key…”
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Designing production-optimal alternative fuels for conventional, flexible-fuel, and ultra-high efficiency engines by König, Andrea, Siska, Maximilian, Schweidtmann, Artur M., Rittig, Jan G., Viell, Jörn, Mitsos, Alexander, Dahmen, Manuel

“…[Display omitted] •Model-based fuel design for conventional, flex fuel, ultra-high efficiency SI engines.•Production cost and GWI of optimized, selectively…”
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ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction by Felton, Kobi C., Raßpe-Lange, Lukas, Rittig, Jan G., Leonhard, Kai, Mitsos, Alexander, Meyer-Kirschner, Julian, Knösche, Carsten, Lapkin, Alexei A.

“…The Perturbed Chain Polar Statistical Associating Fluid Theory (PCP-SAFT) equation of state (EoS) is widely used to predict fluid-phase thermodynamics, but…”
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Fuel Ignition Delay Maps for Molecularly Controlled Combustion by Neumann, Marcel, Rittig, Jan G., Letaief, Ahmed Ben, Honecker, Christian, Ackermann, Philipp, Mitsos, Alexander, Dahmen, Manuel, Pischinger, Stefan

“…Molecularly controlled combustion systems (MCCSs) combine the advantages of compression-ignition and spark-ignition engines by employing both a low reactivity…”
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Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning by Fleitmann, Lorenz, Ackermann, Philipp, Schilling, Johannes, Kleinekorte, Johanna, Rittig, Jan G., vom Lehn, Florian, Schweidtmann, Artur M., Pitsch, Heinz, Leonhard, Kai, Mitsos, Alexander, Bardow, André, Dahmen, Manuel

“…Co-design of alternative fuels and future spark-ignition (SI) engines allows very high engine efficiencies to be achieved. To tailor the fuel’s molecular…”
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Parameter estimation and dynamic optimization of an industrial fed-batch reactor by Rittig, Jan G., Schulze, Jan C., Henrichfreise, Lars, Recker, Sebastian, Feller, Rolf, Mitsos, Alexander, Mhamdi, Adel

“…Modeling and optimization of fed-batch reactors with several multi-step reaction pathways is challenging due to the nonlinear dynamic system behavior and large…”
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Thermodynamics-Consistent Graph Neural Networks by Rittig, Jan G, Mitsos, Alexander

“…We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting composition-dependent activity coefficients of binary mixtures. The GE-GNN…”
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Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks by Brozos, Christoforos, Rittig, Jan G, Akanny, Elie, Bhattacharya, Sandip, Kohlmann, Christina, Mitsos, Alexander

“…Surfactants are key ingredients in foaming and cleansing products across various industries such as personal and home care, industrial cleaning, and more, with…”
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Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks by Brozos, Christoforos, Rittig, Jan G, Bhattacharya, Sandip, Akanny, Elie, Kohlmann, Christina, Mitsos, Alexander

“…The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in industry. Recently, classical QSPR and…”
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Graph Neural Networks for Surfactant Multi-Property Prediction by Brozos, Christoforos, Rittig, Jan G, Bhattacharya, Sandip, Akanny, Elie, Kohlmann, Christina, Mitsos, Alexander

“…Surfactants are of high importance in different industrial sectors such as cosmetics, detergents, oil recovery and drug delivery systems. Therefore, many…”
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GraphXForm: Graph transformer for computer-aided molecular design with application to extraction by Pirnay, Jonathan, Rittig, Jan G, Wolf, Alexander B, Grohe, Martin, Burger, Jakob, Mitsos, Alexander, Grimm, Dominik G

“…Generative deep learning has become pivotal in molecular design for drug discovery and materials science. A widely used paradigm is to pretrain neural networks…”
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Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction by Rittig, Jan G, Felton, Kobi C, Lapkin, Alexei A, Mitsos, Alexander

“…We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem…”
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Graph neural networks for the prediction of molecular structure-property relationships by Rittig, Jan G, Gao, Qinghe, Dahmen, Manuel, Mitsos, Alexander, Schweidtmann, Artur M

“…Machine Learning and Hybrid Modelling for Reaction Engineering, Royal Society of Chemistry, ISBN 978-1-83916-563-4, 159-181, 2023 Molecular property prediction…”
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