48 citations · 56 across the 8 of their papers we have counts for
8 papers
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…
Thermodynamically consistent machine learning model for excess Gibbs energy
Marco Hoffmann, Thomas Specht, Quirin Göttl +4
The excess Gibbs energy plays a central role in chemical engineering and chemistry, providing a basis for modeling thermodynamic properties of liquid mixtures. Predicting the exces…
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…
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…
SetPINNs: Set-based Physics-informed Neural Networks
Mayank Nagda, Phil Ostheimer, Thomas Specht +5
Physics-Informed Neural Networks (PINNs) solve partial differential equations using deep learning. However, conventional PINNs perform pointwise predictions that neglect dependenci…
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…