Prediction of parameters of group contribution models of mixtures by matrix completion

Fabian Jirasek, Nicolas Hayer, Rima Abbas, Bastian Schmid and Hans Hasse

Phys. Chem. Chem. Phys., 2023, 25, 1054
DOI: 10.1039/D2CP04478A

Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning

Guzhong Chen, Zhen Song, Zhiwen Qi and Kai Sundmacher

Digital Discovery, 2023, 2, 591
DOI: 10.1039/D3DD00040K

A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing

Benedikt Winter, Clemens Winter, Johannes Schilling and André Bardow

Digital Discovery, 2022, 1, 859
DOI: 10.1039/D2DD00058J

Understanding the language of molecules: predicting pure component parameters for the PC-SAFT equation of state from SMILES

Benedikt Winter, Philipp Rehner, Timm Esper, Johannes Schilling and André Bardow

Digital Discovery, 2025, 4, 1142
DOI: 10.1039/D4DD00077C

Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution

Edgar Ivan Sanchez Medina, Steffen Linke, Martin Stoll and Kai Sundmacher

Digital Discovery, 2023, 2, 781
DOI: 10.1039/D2DD00142J

HANNA: hard-constraint neural network for consistent activity coefficient prediction

Thomas Specht, Mayank Nagda, Sophie Fellenz, Stephan Mandt, Hans Hasse and Fabian Jirasek

Chem. Sci., 2024, 15, 19777
DOI: 10.1039/D4SC05115G

Balancing molecular information and empirical data in the prediction of physico-chemical properties

Johannes Zenn, Dominik Gond, Fabian Jirasek and Robert Bamler

Digital Discovery, 2025, 4, 683
DOI: 10.1039/D4DD00154K

Prediction of activity coefficients by similarity-based imputation using quantum-chemical descriptors

Nicolas Hayer, Thomas Specht, Justus Arweiler, Dominik Gond, Hans Hasse and Fabian Jirasek

Phys. Chem. Chem. Phys., 2025, 27, 4307
DOI: 10.1039/D4CP04341C

Enhancing predictive models for solubility in multicomponent solvent systems using semi-supervised graph neural networks

Hojin Jung, Christopher D. Stubbs, Sabari Kumar, Raúl Pérez-Soto, Su-min Song, Yeonjoon Kim and Seonah Kim

Digital Discovery, 2025, 4, 1492
DOI: 10.1039/D5DD00015G

Gibbs–Duhem-informed neural networks for binary activity coefficient prediction

Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin and Alexander Mitsos

Digital Discovery, 2023, 2, 1752
DOI: 10.1039/D3DD00103B

Thermodynamics-consistent graph neural networks

Jan G. Rittig and Alexander Mitsos

Chem. Sci., 2024, 15, 18504
DOI: 10.1039/D4SC04554H

Machine learning for determination of activity of water and activity coefficients of electrolytes in binary solutions

Guillaume Zante

Artificial Intelligence Chemistry, 2024, 2, 100069
DOI: 10.1016/j.aichem.2024.100069

Graph neural networks for CO2 solubility predictions in Deep Eutectic Solvents

Gabriel Hernández Morales, Edgar Ivan Sanchez Medina, Arturo Jiménez-Gutiérrez and Victor M. Zavala

Computers & Chemical Engineering, 2024, 187, 108750
DOI: 10.1016/j.compchemeng.2024.108750

An Overview of Computer‐aided Molecular and Process Design

Ashfaq Iftakher, Mohammed Sadaf Monjur and M. M. Faruque Hasan

Chemie Ingenieur Technik, 2023, 95, 315
DOI: 10.1002/cite.202200172

Fairer benchmark of group contribution and machine learning models for property prediction: A new data splitting strategy

Adem R.N. Aouichaoui, Jingkang Liang, Jens Abildskov and Gürkan Sin

Computers & Chemical Engineering, 2025, 202, 109271
DOI: 10.1016/j.compchemeng.2025.109271

Predicting solvation free energies for neutral molecules in any solvent with openCOSMO-RS

Simon Müller, Thomas Nevolianis, Miquel Garcia-Ratés, Christoph Riplinger, Kai Leonhard and Irina Smirnova

Fluid Phase Equilibria, 2025, 589, 114250
DOI: 10.1016/j.fluid.2024.114250

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures

Fabian Jirasek and Hans Hasse

Annual Review of Chemical and Biomolecular Engineering, 2023, 14, 31
DOI: 10.1146/annurev-chembioeng-092220-025342

Modeling and experimental data of LLE, VLE, kinematic Viscosity, and density for the 2-Phenylethanol + n-Heptane mixture at low pressure

Juan D. Henao, Jorge A. Velásquez, Luis F. Cardona and Luis A. Forero

The Journal of Chemical Thermodynamics, 2025, 205, 107459
DOI: 10.1016/j.jct.2025.107459

MLPROP – An Interactive Web Interface for Thermophysical Property Prediction with Machine Learning

Marco Hoffmann, Thomas Specht, Nicolas Hayer, Hans Hasse and Fabian Jirasek

Chemie Ingenieur Technik, 2025
DOI: 10.1002/cite.70004

HybridGamma: A thermodynamically consistent framework for hybrid modelling of activity coefficients

Ulderico Di Caprio, Jan Degrève, Peter Hellinckx, Steffen Waldherr and M. Enis Leblebici

Chemical Engineering Journal, 2023, 475, 146104
DOI: 10.1016/j.cej.2023.146104

SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients

Benedikt Winter, Clemens Winter, Timm Esper, Johannes Schilling and André Bardow

Fluid Phase Equilibria, 2023, 568, 113731
DOI: 10.1016/j.fluid.2023.113731

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

Kobi C. Felton, Lukas Raßpe-Lange, Jan G. Rittig, Kai Leonhard, Alexander Mitsos, Julian Meyer-Kirschner, Carsten Knösche and Alexei A. Lapkin

Chemical Engineering Journal, 2024, 492, 151999
DOI: 10.1016/j.cej.2024.151999

Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks

Christoforos Brozos, Jan G. Rittig, Elie Akanny, Sandip Bhattacharya, Christina Kohlmann and Alexander Mitsos

Computers & Chemical Engineering, 2025, 198, 109085
DOI: 10.1016/j.compchemeng.2025.109085

Predicting Temperature‐Dependent Activity Coefficients at Infinite Dilution Using Tensor Completion

Julie Damay, Gleb Ryzhakov, Fabian Jirasek, Hans Hasse, Ivan Oseledets and Michael Bortz

Chemie Ingenieur Technik, 2023, 95, 1061
DOI: 10.1002/cite.202200230

Graph neural networks for surfactant multi-property prediction

Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, Elie Akanny, Christina Kohlmann and Alexander Mitsos

Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2024, 694, 134133
DOI: 10.1016/j.colsurfa.2024.134133

Nicolas Hayer, Thomas Specht, Justus Arweiler, Hans Hasse and Fabian Jirasek

J. Phys. Chem. A, 2025, 129, 3141
DOI: 10.1021/acs.jpca.4c08360

Multi-fidelity graph neural networks for predicting toluene/water partition coefficients

Thomas Nevolianis, Jan G. Rittig, Alexander Mitsos and Kai Leonhard

J Cheminform, 2025, 17
DOI: 10.1186/s13321-025-01057-6

Hierarchical matrix completion for the prediction of properties of binary mixtures

Dominik Gond, Jan-Tobias Sohns, Heike Leitte, Hans Hasse and Fabian Jirasek

Computers & Chemical Engineering, 2025, 199, 109122
DOI: 10.1016/j.compchemeng.2025.109122

Integrated design of solvent–antisolvent mixtures and crystallization processes powered by machine learning

Luca Bosetti, Benedikt Winter, Johanna Lindfeld and André Bardow

Computers & Chemical Engineering, 2025, 202, 109272
DOI: 10.1016/j.compchemeng.2025.109272

Pooling solvent mixtures for solvation free energy predictions

Roel J. Leenhouts, Nathan Morgan, Emad Al Ibrahim, William H. Green and Florence H. Vermeire

Chemical Engineering Journal, 2025, 513, 162232
DOI: 10.1016/j.cej.2025.162232

Physical pooling functions in graph neural networks for molecular property prediction

Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard and Alexander Mitsos

Computers & Chemical Engineering, 2023, 172, 108202
DOI: 10.1016/j.compchemeng.2023.108202

Challenges in data-based reactor modeling: A critical analysis of purely data-driven and hybrid models for a CSTR case study

Luisa Peterson, Jens Bremer and Kai Sundmacher

Computers & Chemical Engineering, 2024, 184, 108643
DOI: 10.1016/j.compchemeng.2024.108643

Prediction of the density of aqueous electrolyte solutions with matrix completion methods

Maximilian Kohns, Pascal Zittlau and Fabian Jirasek

Fluid Phase Equilibria, 2025, 597, 114454
DOI: 10.1016/j.fluid.2025.114454

Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks

Jinke Sun, Jianfei Xue, Guangyu Yang, Jingde Li and Wei Zhang

AIChE Journal, 2025, 71
DOI: 10.1002/aic.18637

The a priori screening of potential organic solvents using artificial neural networks

Nivaar Brijmohan, Kuveneshan Moodley and Caleb Narasigadu

Fluid Phase Equilibria, 2024, 577, 113960
DOI: 10.1016/j.fluid.2023.113960

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks

Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, Elie Akanny, Christina Kohlmann and Alexander Mitsos

J. Chem. Theory Comput., 2024, 20, 5695
DOI: 10.1021/acs.jctc.4c00314

Prediction of Electrical Conductivity of Ionic Liquids: From COSMO-RS Derived QSPR Evaluation to Boosting Machine Learning

Zixin Chen, Jiahui Chen, Yuxin Qiu, Jie Cheng, Long Chen, Zhiwen Qi and Zhen Song

ACS Sustainable Chem. Eng., 2024, 12, 6648
DOI: 10.1021/acssuschemeng.4c00307

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids

Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen and Alexander Mitsos

Computers & Chemical Engineering, 2023, 171, 108153
DOI: 10.1016/j.compchemeng.2023.108153

GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures

Marco Hoffmann, Hans Hasse and Fabian Jirasek

Chemical Engineering Journal Advances, 2025, 22, 100750
DOI: 10.1016/j.ceja.2025.100750

Solvation Free Energies of Anions: From Curated Reference Data to Predictive Models

Thomas Nevolianis, Jonathan W Zheng, Simon Müller, Matthias Baumann, Sofja Tshepelevitsh, Ivari Kaljurand, Ivo Leito, Irina Smirnova, William H Green and Kai Leonhard

J. Am. Chem. Soc., 2025, 147, 30626
DOI: 10.1021/jacs.5c02578

Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution

Edgar Ivan Sanchez Medina, Sreekanth Kunchapu and Kai Sundmacher

J. Phys. Chem. A, 2023, 127, 9863
DOI: 10.1021/acs.jpca.3c05892

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

Zhen Song, Jiahui Chen, Jie Cheng, Guzhong Chen and Zhiwen Qi

Chem. Rev., 2024, 124, 248
DOI: 10.1021/acs.chemrev.3c00223

An Interpretable Solute–Solvent Interactive Attention Module Intensified Graph-Learning Architecture toward Enhancing the Prediction Accuracy of an Infinite Dilution Activity Coefficient

Di Wu, Zutao Zhu, Jun Zhang, Huaqiang Wen, Saimeng Jin and Weifeng Shen

Ind. Eng. Chem. Res., 2024, 63, 8741
DOI: 10.1021/acs.iecr.4c00107

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

Lorenz Fleitmann, Philipp Ackermann, Johannes Schilling, Johanna Kleinekorte, Jan G. Rittig, Florian vom Lehn, Artur M. Schweidtmann, Heinz Pitsch, Kai Leonhard, Alexander Mitsos, André Bardow and Manuel Dahmen

Energy Fuels, 2023, 37, 2213
DOI: 10.1021/acs.energyfuels.2c03296