84 citations · 360 across the 16 of their papers we have counts for
19 papers
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…
A Systematic Evaluation of Molecular Mixture Behavior Prediction
Roel J. Leenhouts, Nathan K. Morgan, William Green +2
Machine learning for molecular property prediction has focused largely on pure compounds, even though many practical applications depend on mixtures with intermolecular interaction…
Tabular foundation models for in-context prediction of molecular properties
Karim K. Ben Hicham, Jan G. Rittig, Martin Grohe +1
Accurate molecular property prediction is central to drug discovery, catalysis, and process design, yet real-world applications are often limited by small datasets. Molecular found…
Differentiable Thermodynamic Phase-Equilibria for Machine Learning
Karim K. Ben Hicham, Moreno Ascani, Jan G. Rittig +1
Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure in…
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…
Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
Yuanqi Du, Botao Yu, Tianyu Liu +25
There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by s…
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…
Federated Learning from Molecules to Processes: A Perspective
Jan G. Rittig, Clemens Kortmann
We present a perspective on federated learning in chemical engineering that envisions collaborative efforts in machine learning (ML) developments within the chemical industry. Larg…
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…
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…
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber +4
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…
Graph neural networks for the prediction of molecular structure-property relationships
Jan G. Rittig, Qinghe Gao, Manuel Dahmen +2
Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quanti…
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann +2
Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MC…
Graph Machine Learning for Design of High-Octane Fuels
Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann +7
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 p…