Zobrazeno 1 - 10
of 2 481
pro vyhledávání: '"A, Rittig"'
Autor:
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 the critical micelle concentration (CMC) being of major interest. Predictive models for CM
Externí odkaz:
http://arxiv.org/abs/2411.02224
Autor:
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 on string representations of molecules and fine-tune them using reinforcement learning on
Externí odkaz:
http://arxiv.org/abs/2411.01667
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:
Leticia Forny Germano, Jacqueline A. Koehler, Laurie L. Baggio, Fiona Cui, Chi Kin Wong, Nikolaj Rittig, Xiemin Cao, Dianne Matthews, Daniel J. Drucker
Publikováno v:
Molecular Metabolism, Vol 89, Iss , Pp 102019- (2024)
Objective: The development of glucagon-like peptide-1 receptor (GLP-1R) agonists for the treatment of type 2 diabetes and obesity has been accompanied by evidence for anti-inflammatory and cytoprotective actions in the heart, blood vessels, kidney, a
Externí odkaz:
https://doaj.org/article/251c6b672668429ebb6e64cd03609235
Autor:
Yakai Liu, Zhenwei Zhang, Huijie Hu, Xiangfei He, Pengchao Xu, Qifeng Dou, Cuiping Song, Huiqing Zhang, Israel Franco, Konstantinos Kamperis, Søren Rittig, Jianguo Wen
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract The purpose of this study was to investigate the prevalence and relevant factors of nocturia and its impact on sleep quality in university students in Mainland China. A large-scale survey was conducted on 14,000 university students from 3 un
Externí odkaz:
https://doaj.org/article/5d11202bf23d410d822d71f0d8f9f4a3
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