3 papers
cs.LG2026
Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning
Zeno Romero, Maximilian Kohns, Fabian Jirasek
Activities in aqueous electrolyte solutions, usually described by ionic activity and osmotic coefficients, are important properties for modeling many processes in industry and natu…
physics.chem-ph2026
Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids
Jens Wagner, Zeno Romero, Kerstin Münnemann +4
Diffusion coefficients are key thermophysical properties for modeling mass transport in liquids, but experimental data are scarce, making reliable prediction methods indispensable.…
cs.LG2026
Prediction of Diffusion Coefficients in Mixtures with Tensor Completion
Zeno Romero, Kerstin Münnemann, Hans Hasse +1
Predicting diffusion coefficients in mixtures is crucial for many applications, as experimental data remain scarce, and machine learning (ML) offers promising alternatives to estab…