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Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data by Gianluca Armeli; Jan-Hendrik Peters; Thomas Koop is a Engineering article available to read on EtoBox.

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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 t

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Author

Gianluca Armeli; Jan-Hendrik Peters; Thomas Koop

Publisher

American Chemical Society (ACS)

Published

2023

Language

EN

Field

Engineering (Physical Sciences)