Evaluating the effect of curing conditions on the glass transition of the structural adhesive using conditional tabular generative adversarial networks

Wang, Songbo; Yang, Haixin; Stratford, Tim; He, Jiayi; Li, Biao; Su, Jun

doi:10.1016/j.engappai.2023.107796

Cited by 4 publications

(1 citation statement)

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“…One such crucial property is the glass transition temperature of polymers. The glass transition temperature (Tg) is usually characterized as the temperature range at which the polymer makes a shift from a rigid, glass-like state to a more flexible, elastic state [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. The Tg value of a polymer is acknowledged to be affected by the polymer’s chain mobility or volume without chains.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers

Uddin,

Fan

2024

Polymers

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The glass transition temperature of polymers is a key parameter in meeting the application requirements for energy absorption. Previous studies have provided some data from slow, expensive trial-and-error procedures. By recognizing these data, machine learning algorithms are able to extract valuable knowledge and disclose essential insights. In this study, a dataset of 7174 samples was utilized. The polymers were numerically represented using two methods: Morgan fingerprint and molecular descriptor. During preprocessing, the dataset was scaled using a standard scaler technique. We removed the features with small variance from the dataset and used the Pearson correlation technique to exclude the features that were highly connected. Then, the most significant features were selected using the recursive feature elimination method. Nine machine learning techniques were employed to predict the glass transition temperature and tune their hyperparameters. The models were compared using the performance metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). We observed that the extra tree regressor provided the best results. Significant features were also identified using statistical machine learning methods. The SHAP method was also employed to demonstrate the influence of each feature on the model’s output. This framework can be adaptable to other properties at a low computational expense.

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