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