| Summary: | One crucial factor in polymer material fabrication is its melting temperature, which is key to determining the manufacturing process. However, difficulties have been encountered in finding correlations between the melting temperature and influencing factors. Therefore, machine learning is employed to discern the relationship between polymer structure and its melting temperature. The models used include KNN, SVR, and XGB, as well as deep learning with ANN and NLP. The ability of SMILES and NLP descriptors in describing polymer structures is also examined. The input length of the models varied between 50 and 200. All models produce the most accurate predictions with an input length of 200. KNN and SVR generate relatively stable but consistently low R-squared values, with MAE values above 40. ANN and NLP exhibit high R-squared values in some trials but with low stability and relatively high MAE values. XGBoost consistently produces higher and more stable R2 values. The best-performing XGBoost model is validated using SMILES strings outside the dataset, demonstrating its predictive ability with an MAE of 24.09, deemed sufficient for predicting melting temperatures. © 2024 IEEE.
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