Work

Artificial intelligence in thermodynamics: hybrid modeling of thermophysical properties of fluids

Year: 2026

Type: review

Abstract: Artificial intelligence is currently transforming thermodynamics. Hybrid models that combine machine learning (ML) with physical modeling enable predictions of thermophysical properties with unprecedented scope and accuracy. Focusing on the thermophysical properties of fluids, recent advances in this field are highlighted, covering two hybridization techniques: (i) embedding ML into physical models and (ii) incorporating physical knowledge into ML models. The discussion covers different types of thermodynamic models: (i) excess Gibbs energy models, (ii) equations of state, and (iii) force field models. The new hybrid models combine the soundness of physical models with the flexibility of ML models and give the best results when trained on large data sets, which are, however, not always available. The new hybrid models often significantly outperform widely used classical physical thermodynamic benchmark models. We have only begun to explore the new routes opened up by hybrid thermodynamic modeling; this review provides a starting point for future work in this field.

Source: Current Opinion in Chemical Engineering

DOI: 10.1016/j.coche.2026.101236

Authors: Hans Hasse, Sebastian Schmitt, Fabian Jirasek

Institutions: University of Kaiserslautern, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau

Language: English

Topic: Phase Equilibria and Thermodynamics

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,250

Funders: Carl-Zeiss-Stiftung, Deutsche Forschungsgemeinschaft