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

ABSTRACT: 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

SUBMITTER: Armeli G 

PROVIDER: S-EPMC10077449 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature