4 papers
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…
Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties
Johannes Zenn, Dominik Gond, Fabian Jirasek +1
Predicting the physico-chemical properties of pure substances and mixtures is a central task in thermodynamics. Established prediction methods range from fully physics-based ab-ini…
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…