84 citations · 360 across the 16 of their papers we have counts for
8 papers · 1 filter
A systematic comparison of hard- and soft-constrained physics-informed molecular machine learning with the Clapeyron equation
Jan Pavšek, Jan G. Rittig, Alexander Mitsos
In molecular machine learning (ML), significant progress has been made on integrating fundamental thermodynamic relations into ML models. Such physics-informed approaches can be ca…
Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
Jan Pavšek, Alexander Mitsos, Elvis J. Sim +1
Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce ex…
DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
Jan Pavšek, Alexander Mitsos, Manuel Dahmen +2
We propose a machine learning (ML) architecture to better capture the dependency of thermodynamic properties on the independent states. When predicting state-dependent thermodynami…
Molecular Machine Learning in Chemical Process Design
Jan G. Rittig, Manuel Dahmen, Martin Grohe +2
We present a perspective on molecular machine learning (ML) in the field of chemical process engineering. Recently, molecular ML has demonstrated great potential in (i) providing h…
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…
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…
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin +1
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…