29 citations · 53 across the 4 of their papers we have counts for
4 papers
MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning
Marco Hoffmann, Thomas Specht, Nicolas Hayer +2
Machine learning (ML) enables the development of powerful methods for predicting thermophysical properties with unprecedented scope and accuracy. However, technical barriers like c…
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…
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…