3 papers
physics.chem-ph2026
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…
physics.chem-ph2026
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…
physics.chem-ph2025
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…