Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data

Armeli Iapichino G, Peters J-H, Koop T (2023)
ACS Omega 8(13): 12298-12309.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Knowledge of the glass transition temperature of molecular compounds that occur in atmospheric aerosol particles is important for estimating their viscosity, as it directly influences the kinetics of chemical reactions and particle phase state. While there is a great diversity of organic compounds present in aerosol particles, for only a minor fraction of them experimental glass transition temperatures are known. Therefore, we have developed a machine learning model designed to predict the glass transition temperature of organic molecular compounds based on molecule-derived input variables. The extremely randomized trees (extra trees) procedure was chosen for this purpose. Two approaches using different sets of input variables were followed. The first one uses the number of selected functional groups present in the compound, while the second one generates descriptors from a SMILES (Simplified Molecular Input Line Entry System) string. Organic compounds containing carbon, hydrogen, oxygen, nitrogen, and halogen atoms are included. For improved results, both approaches can be combined with the melting temperature of the compound as an additional input variable. The results show that the predictions of both approaches show a similar mean absolute error of about 12–13 K, with the SMILES-based predictions performing slightly better. In general, the model shows good predictive power considering the diversity of the experimental input data. Furthermore, we also show that its performance exceeds that of previous parameterizations developed for this purpose and also performs better than existing machine learning models. In order to provide user-friendly versions of the model for applications, we have developed a web site where the model can be run by interested scientists via a web-based interface without prior technical knowledge. We also provide Python code of the model. Additionally, all experimental input data are provided in form of the Bielefeld Molecular Organic Glasses (BIMOG) database. We believe that this model is a powerful tool for many applications in atmospheric aerosol science and material science.
Erscheinungsjahr
2023
Zeitschriftentitel
ACS Omega
Band
8
Ausgabe
13
Seite(n)
12298-12309
eISSN
2470-1343
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2977986

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Armeli Iapichino G, Peters J-H, Koop T. Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data. ACS Omega. 2023;8(13):12298-12309.
Armeli Iapichino, G., Peters, J. - H., & Koop, T. (2023). Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data. ACS Omega, 8(13), 12298-12309. https://doi.org/10.1021/acsomega.2c08146
Armeli Iapichino, Gianluca, Peters, Jan-Hendrik, and Koop, Thomas. 2023. “Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data”. ACS Omega 8 (13): 12298-12309.
Armeli Iapichino, G., Peters, J. - H., and Koop, T. (2023). Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data. ACS Omega 8, 12298-12309.
Armeli Iapichino, G., Peters, J.-H., & Koop, T., 2023. Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data. ACS Omega, 8(13), p 12298-12309.
G. Armeli Iapichino, J.-H. Peters, and T. Koop, “Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data”, ACS Omega, vol. 8, 2023, pp. 12298-12309.
Armeli Iapichino, G., Peters, J.-H., Koop, T.: Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data. ACS Omega. 8, 12298-12309 (2023).
Armeli Iapichino, Gianluca, Peters, Jan-Hendrik, and Koop, Thomas. “Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data”. ACS Omega 8.13 (2023): 12298-12309.
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2023-04-04T10:04:50Z
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