Machine Learning in Thermodynamics

Accurate prediction of thermodynamic properties is of central importance to the chemical industry, as experimental data are often scarce. While traditional physical models are limited in scope and predictive accuracy, machine learning methods offer a promising approach to overcoming these challenges. In particular, hybrid methods that combine the strengths of physical modeling and machine learning have achieved remarkable results in recent years. In my project, I investigate the potential of these hybrid approaches and explore how thermodynamic knowledge can be systematically integrated into machine learning models to further improve their predictive accuracy and robustness.

Publications

• M. Hoffmann, T. Specht, N. Hayer, H. Hasse, F. Jirasek: MLPROP – An Interactive Web Interface for Thermophysical Property Prediction with Machine Learning, Chemie Ingenieur Technik (2025). [DOI]
• N. Hayer, H. Hasse, F. Jirasek: Modified UNIFAC 2.0 – Completing Interaction Parameter Tables with Machine Learning, Industrial & Engineering Chemistry Research, 64 (2025) 10304–10313. [DOI]
• N. Hayer, T. Specht, J. Arweiler, H. Hasse, F. Jirasek: Similarity-Informed Matrix Completion Method for Predicting Activity Coefficients, The Journal of Physical Chemistry A, 129 (2025) 3141–3147. [DOI]
• N. Hayer, T. Specht, J. Arweiler, D. Gond, H. Hasse, F. Jirasek: Prediction of activity coefficients by similarity-based imputation using quantum-chemical descriptors, Physical Chemistry Chemical Physics 27 (2025) 4307-4315. [DOI]
• N. Hayer, H. Hasse, F. Jirasek: Prediction of Temperature-Dependent Henry’s Law Constants by Matrix Completion, The Journal of Physical Chemistry B 129 (2025) 409-416. [DOI]
• N. Hayer, T. Wendel, S. Mandt, H. Hasse, F. Jirasek: Advancing thermodynamic group-contribution methods by machine learning: UNIFAC 2.0, Chemical Engineering Journal 504 (2025) 158667. [DOI]
• M. Hoffmann, N. Hayer, M. Kohns, F. Jirasek, H. Hasse: Prediction of pair interactions in mixtures by matrix completion, Physical Chemistry Chemical Physics 26 (2024) 19390-19397. [DOI]
• F. Jirasek, N. Hayer, R. Abbas, B. Schmid, H. Hasse: Prediction of Parameters of Group Contribution Models of Mixtures by Matrix Completion, Physical Chemistry Chemical Physics 25 (2023) 1054-1062. [DOI]
• O. Großmann, D. Bellaire, N. Hayer, F. Jirasek, H. Hasse: Database for liquid phase diffusion coefficients at infinite dilution at 298 K and matrix completion methods for their prediction, Digital Discovery 1 (2022) 886-897. [DOI]
• N. Hayer, F. Jirasek, H. Hasse: Prediction of Henry's law constants by matrix completion, AIChE Journal 68 (2022) e17753. [DOI]
• N. Hayer, M. Kohns: Thermodynamically Rigorous Description of the Open Circuit Voltage of Redox Flow Batteries, Journal of the Electrochemical Society 167 (2020) 110516. [DOI]

Talks

• L. Vollmer, R. Loubet, P. Zittlau, M. Hoffmann, N. Hayer, S. Fellenz, F. Jirasek, H. Leitte, H. Hasse: Translating Thermodynamic Knowledge to Computers, Thermodynamik-Kolloquium, Stuttgart, 25.-27.09.2024.
• N. Hayer, S. Mandt, H. Hasse, F. Jirasek: Embedding Machine Learning Methods in Physical Thermodynamic Models, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2024, Vilnius, Lithuania, 09.-13.09.2024.
• N. Hayer, H. Hasse, F. Jirasek: Advancing Group-Contribution Methods for Thermophysical Properties of Mixtures, 22nd Symposium on Thermophysical Properties, Boulder (CO), USA, 24.-28.06.2024.
• N. Hayer, H. Hasse, F. Jirasek: Extending the Scope of Thermodynamic Group-Contribution Methods by Machine Learning, Thermodynamik-Kolloquium, Hannover, 25.-27.09.2023.
• N. Hayer, F. Jirasek, H. Hasse: Hybrid Group-Contribution Methods for Predicting Thermodynamic Properties of Mixtures, 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology, Berlin, 17.-21.09.2023.
• N. Hayer, F. Jirasek, H. Hasse: Combining Machine Learning with Thermodynamic Group-Contribution Methods, ECTP2023 – 22nd European Conference on Thermophysical Properties, Venice, Italy, 10.-13.09.2023.
• N. Hayer, T. Specht, J. Arweiler, H. Hasse, F. Jirasek: Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors, Thermodynamik-Kolloquium, Chemnitz, 26.-28.09.2022.
• N. Hayer, F. Jirasek: Learning from Netflix – Recommender Systems for Predicting Thermodynamic Properties, Young Researchers Symposium, Kaiserslautern, 22.07.2022.
• F. Jirasek, N. Hayer, T. Specht, J. Damay, M. Bortz, R. Abbas, B. Schmid, H. Hasse: Hybrid Predictive Fluid Property Models – Integration of Physical Knowledge in Data-driven Matrix Completion Methods, 13th European Congress of Chemical Engineering and 6th European Congress of Applied Biotechnology, Web-Conference, 20.-23.09.2021.

Posters

• N. Hayer, S. Mandt, H. Hasse, F. Jirasek: Advancing Group Contribution Models by Machine Learning: UNIFAC 2.0, International Conference on Properties and Phase Equilibria for Product and Process Design, Bad Gögging, 11.-15.05.2025.
• N. Hayer, S. Mandt, H. Hasse, F. Jirasek: Advancing Group Contribution Models by Machine Learning: UNIFAC 2.0, Thermodynamik-Kolloquium, Stuttgart, 25.-27.09.2024.
• N. Hayer, S. Mandt, H. Hasse, F. Jirasek: Embedding Machine Learning Methods in Physical Thermodynamic Models, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2024, Vilnius, Lithuania, 09.-13.09.2024.
• N. Hayer, M. Kohns, F. Jirasek, H. Hasse: Active Learning for the Prediction of Second Virial Coefficients in Mixtures, Thermodynamik-Kolloquium, Hannover, 25.-27.09.2023.
• N. Hayer, F. Jirasek, H. Hasse: Prediction of Temperature-Dependent Henry's Law Constants by Matrix Completion, Thermodynamik-Kolloquium, Chemnitz, 26.-28.09.2022.
• O. Großmann, D. Bellaire, N. Hayer, F. Jirasek, H. Hasse: Prediction of Diffusion Coefficients at Infinite Dilution by Matrix Completion, ProcessNet and DECHEMA-BioTechNet Jahrestagungen 2022 together with 13th ESBES Symposium, Aachen, 12.-15.09.2022.
• N. Hayer, F. Jirasek, H. Hasse: Prediction of Henry's Law Constants by Matrix Completion, ProcessNet and DECHEMA-BioTechNet Jahrestagungen 2022 together with 13th ESBES Symposium, Aachen, 12.-15.09.2022.
• N. Hayer, F. Jirasek, H. Hasse: Prediction of Temperature-Dependent Henry's Law Constants by Matrix Completion, European Symposium on Applied Thermodynamics (ESAT), Graz, Austria, 17.-20.07.2022.
• O. Großmann, D. Bellaire, N. Hayer, F. Jirasek, H. Hasse: Establishment of a Database and Prediction of Diffusion Coefficients at Infinite Dilution, Thermodynamik-Kolloquium, Web-Conference, 27.-29.09.2021.
• N. Hayer, F. Jirasek, H. Hasse: Prediction of Henry's Law Constants by Matrix Completion, Thermodynamik-Kolloquium, Web-Conference, 27.-29.09.2021.
• N. Hayer, M. Kohns: Thermodynamically Rigorous Description of the Open Circuit Voltage of Redox Flow Batteries, Thermodynamik-Kolloquium, Web-Conference, 27.-29.09.2021.
• N. Hayer, F. Jirasek, H. Hasse: Prediction of Henry's Law Constants by Matrix Completion, European Symposium on Applied Thermodynamics (ESAT), Web-Conference, 05.-09.07.2021.
• N. Hayer, M. Kohns: Thermodynamically Rigorous Description of the Open Circuit Voltage of Redox Flow Batteries, Annual Meeting on Reaction Engineering, Web-Conference, 10.-12.05.2021.