107 citations · 244 across the 23 of their papers we have counts for
3 papers · 1 filter
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
Nicolas Hayer, Thorsten Wendel, Stephan Mandt +2
Accurate prediction of thermodynamic properties is pivotal in chemical engineering for optimizing process efficiency and sustainability. Physical group-contribution (GC) methods ar…
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties
Fabian Jirasek, Robert Bamler, Stephan Mandt
We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach `distills' the physical method's predictions into a p…
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Fabian Jirasek, Rodrigo A. S. Alves, Julie Damay +6
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibr…