Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Rittig, Jan G."'
Autor:
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 architecture ensures thermodynamic consistency by predicting the molar excess Gibbs free ene
Externí odkaz:
http://arxiv.org/abs/2407.18372
Autor:
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 Graph Neural Networks (GNNs), a deep learning technique, have been successfully applied to
Externí odkaz:
http://arxiv.org/abs/2403.03767
Autor:
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 quantitative structure-property relationship (QSPR) models have been developed for surfactants.
Externí odkaz:
http://arxiv.org/abs/2401.01874
We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightfo
Externí odkaz:
http://arxiv.org/abs/2306.07937
Autor:
Schweidtmann, Artur M., Rittig, Jan G., Weber, Jana M., Grohe, Martin, Dahmen, Manuel, Leonhard, Kai, Mitsos, Alexander
Publikováno v:
Computers and Chemical Engineering Volume 172, April 2023, 108202
Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the pooling function which combines atom feature vectors into molecular fi
Externí odkaz:
http://arxiv.org/abs/2207.13779
Publikováno v:
Machine Learning and Hybrid Modelling for Reaction Engineering, Royal Society of Chemistry, ISBN 978-1-83916-563-4, 159-181, 2023
Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quantitative structure-property/activity relationships (QSPRs/QSARs) charact
Externí odkaz:
http://arxiv.org/abs/2208.04852
Publikováno v:
Computers & Chemical Engineering 171, 108153, 2023
Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MCMs), transformers, and graph neural networks (GNNs) have shown high ac
Externí odkaz:
http://arxiv.org/abs/2206.11776
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
Publikováno v:
AIChE Journal 69 (4), e17971, 2023
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
Externí odkaz:
http://arxiv.org/abs/2206.00619
Autor:
Felton, Kobi C., Raßpe-Lange, Lukas, Rittig, Jan G., Leonhard, Kai, Mitsos, Alexander, Meyer-Kirschner, Julian, Knösche, Carsten, Lapkin, Alexei A.
Publikováno v:
In Chemical Engineering Journal 15 July 2024 492
Autor:
Schweidtmann, Artur M., Rittig, Jan G., Weber, Jana M., Grohe, Martin, Dahmen, Manuel, Leonhard, Kai, Mitsos, Alexander
Publikováno v:
In Computers and Chemical Engineering April 2023 172