4/27/2023 0 Comments Equil note pen(11) published a study in which machine learning algorithms (mainly support vector machines and neural networks) were used to predict the glass transition temperature of organic molecular compounds. Most models that were developed in this context are used either for the discovery of new systems (16,17) or for the prediction of a specific property. Within the past decade, the application of machine learning methods in natural sciences has experienced a rapid growth. (8) Nevertheless, this approach is sometimes considered to be only a rule of thumb, in light of the statistical deviation of the various data from the prediction (1σ: ☒1 K, 2σ: ±42 K). Further studies have elaborated that the T g/ T m ratio is ∼0.7 for a large variety of substances. (3−5) A long-known and simple, but surprisingly reliable, method is the Boyer–Beaman rule (6,7) that formulates a proportional relationship between the glass transition temperature T g and the melting temperature T m of a substance. (2) Over the years, several surrogate methods have been developed for predicting the conditions for a glassy state and its transition temperature for molecular organic compounds. (1) Due to its nonequilibrium nature, the glass transition temperature depends on the thermal history of the material, thereby impeding the comparison of experimental T g values and their theoretical prediction. This transition from a liquid to an amorphous solid state is accompanied by a change in heat capacity that can be detected experimentally, e.g., by differential scanning calorimetry. Kinetically, the glass transition temperature T g is defined as the temperature at which the viscosity of a substance reaches a value of about 10 12 Pa s. The glass transition is a nonequilibrium phase transition. We believe that this model is a powerful tool for many applications in atmospheric aerosol science and material science. Additionally, all experimental input data are provided in form of the Bielefeld Molecular Organic Glasses (BIMOG) database. We also provide Python code of the model. 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. 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 general, the model shows good predictive power considering the diversity of the experimental input data. 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. For improved results, both approaches can be combined with the melting temperature of the compound as an additional input variable. Organic compounds containing carbon, hydrogen, oxygen, nitrogen, and halogen atoms are included. 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. Two approaches using different sets of input variables were followed. The extremely randomized trees (extra trees) procedure was chosen for this purpose. 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. 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. 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.
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