48 citations · 56 across the 8 of their papers we have counts for
4 papers · 1 filter
Composition-Dependent Self-Diffusion Coefficients in Liquid Mixtures from Hybrid Machine Learning
Jens Wagner, Thomas Specht, Hans Hasse +1
Self-diffusion coefficients are key descriptors of molecular mobility, yet experimental data remain scarce, highlighting the need for reliable prediction methods. In previous work,…
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.…
CHAOS -- A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors
Dominik Gond, Justus Arweiler, Thomas Specht +2
Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, e…
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…