84 citations · 360 across the 16 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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 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…