48 citations · 124 across the 15 of their papers we have counts for
6 papers · 1 filter
Modified UNIFAC 2.0 -- A Group-Contribution Method Completed with Machine Learning
Nicolas Hayer, Hans Hasse, Fabian Jirasek
Predicting thermodynamic properties of mixtures is a cornerstone of chemical engineering, yet conventional group-contribution (GC) methods like modified UNIFAC (Dortmund) remain li…
Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors
Nicolas Hayer, Thomas Specht, Justus Arweiler +3
In this work, we introduce a novel approach for predicting thermodynamic properties of binary mixtures, which we call the similarity-based method (SBM). The method is based on quan…
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures
Dominik Gond, Jan-Tobias Sohns, Heike Leitte +2
Predicting the thermodynamic properties of mixtures is crucial for process design and optimization in chemical engineering. Machine learning (ML) methods are gaining increasing att…
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…
HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
Thomas Specht, Mayank Nagda, Sophie Fellenz +3
We present the first hard-constraint neural network for predicting activity coefficients (HANNA), a thermodynamic mixture property that is the basis for many applications in scienc…
KnowTD-An Actionable Knowledge Representation System for Thermodynamics
Luisa Vollmer, Sophie Fellenz, Fabian Jirasek +2
We demonstrate that thermodynamic knowledge acquired by humans can be transferred to computers so that the machine can use it to solve thermodynamic problems and produce explainabl…