GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures
Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we have developed GRAPPA — a hybrid graph neural network for pred...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Chemical Engineering Journal Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266682112500047X |
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| author | Marco Hoffmann Hans Hasse Fabian Jirasek |
| author_facet | Marco Hoffmann Hans Hasse Fabian Jirasek |
| author_sort | Marco Hoffmann |
| collection | DOAJ |
| description | Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we have developed GRAPPA — a hybrid graph neural network for predicting vapor pressures of pure components. GRAPPA enables the prediction of the vapor pressure curve of basically any organic molecule, requiring only the molecular structure as input. The new model consists of three parts: A graph attention network for the message passing step, a pooling function that captures long-range interactions, and a prediction head that yields the component-specific parameters of the Antoine equation, from which the vapor pressure can readily and consistently be calculated for any temperature. We have trained and evaluated GRAPPA on experimental vapor pressure data of almost 25,000 pure components. We found excellent prediction accuracy for unseen components, outperforming state-of-the-art group contribution methods and other machine learning approaches in applicability and accuracy. The trained model and its code are fully disclosed, and GRAPPA is directly applicable via the interactive website https://ml-prop.mv.rptu.de/. |
| format | Article |
| id | doaj-art-efb594021079499dbc5919e05a58c466 |
| institution | OA Journals |
| issn | 2666-8211 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Chemical Engineering Journal Advances |
| spelling | doaj-art-efb594021079499dbc5919e05a58c4662025-08-20T02:29:02ZengElsevierChemical Engineering Journal Advances2666-82112025-05-012210075010.1016/j.ceja.2025.100750GRAPPA—A hybrid graph neural network for predicting pure component vapor pressuresMarco Hoffmann0Hans Hasse1Fabian Jirasek2Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, GermanyLaboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, GermanyCorresponding author.; Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, GermanyAlthough the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we have developed GRAPPA — a hybrid graph neural network for predicting vapor pressures of pure components. GRAPPA enables the prediction of the vapor pressure curve of basically any organic molecule, requiring only the molecular structure as input. The new model consists of three parts: A graph attention network for the message passing step, a pooling function that captures long-range interactions, and a prediction head that yields the component-specific parameters of the Antoine equation, from which the vapor pressure can readily and consistently be calculated for any temperature. We have trained and evaluated GRAPPA on experimental vapor pressure data of almost 25,000 pure components. We found excellent prediction accuracy for unseen components, outperforming state-of-the-art group contribution methods and other machine learning approaches in applicability and accuracy. The trained model and its code are fully disclosed, and GRAPPA is directly applicable via the interactive website https://ml-prop.mv.rptu.de/.http://www.sciencedirect.com/science/article/pii/S266682112500047XVapor pressure predictionMachine learningGraph neural networksAntoine equationPhase equilibria |
| spellingShingle | Marco Hoffmann Hans Hasse Fabian Jirasek GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures Chemical Engineering Journal Advances Vapor pressure prediction Machine learning Graph neural networks Antoine equation Phase equilibria |
| title | GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures |
| title_full | GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures |
| title_fullStr | GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures |
| title_full_unstemmed | GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures |
| title_short | GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures |
| title_sort | grappa a hybrid graph neural network for predicting pure component vapor pressures |
| topic | Vapor pressure prediction Machine learning Graph neural networks Antoine equation Phase equilibria |
| url | http://www.sciencedirect.com/science/article/pii/S266682112500047X |
| work_keys_str_mv | AT marcohoffmann grappaahybridgraphneuralnetworkforpredictingpurecomponentvaporpressures AT hanshasse grappaahybridgraphneuralnetworkforpredictingpurecomponentvaporpressures AT fabianjirasek grappaahybridgraphneuralnetworkforpredictingpurecomponentvaporpressures |