37 citations · 91 across the 12 of their papers we have counts for
5 papers · 1 filter
Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
Christoforos Brozos, Jan G. Rittig, Elie Akanny +3
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…
GraphXForm: Graph transformer for computer-aided molecular design
Jonathan Pirnay, Jan G. Rittig, Alexander B. Wolf +4
Generative deep learning has become pivotal in molecular design for drug discovery, materials science, and chemical engineering. A widely used paradigm is to pretrain neural networ…
Thermodynamics-Consistent Graph Neural Networks
Jan G. Rittig, Alexander Mitsos
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 t…
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya +3
The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in industry. Recently, classical QSPR and Graph Neural Network…
Graph Neural Networks for Surfactant Multi-Property Prediction
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya +3
Surfactants are of high importance in different industrial sectors such as cosmetics, detergents, oil recovery and drug delivery systems. Therefore, many quantitative structure-pro…