Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning

Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (Tg) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for meas...

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Published in:POLYMERS
Main Authors: Fatriansyah, Jaka Fajar; Linuwih, Baiq Diffa Pakarti; Andreano, Yossi; Sari, Intan Septia; Federico, Andreas; Anis, Muhammad; Surip, Siti Norasmah; Jaafar, Mariatti
Format: Article
Language:English
Published: MDPI 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001311422500001
author Fatriansyah
Jaka Fajar; Linuwih
Baiq Diffa Pakarti; Andreano
Yossi; Sari
Intan Septia; Federico
Andreas; Anis
Muhammad; Surip
Siti Norasmah; Jaafar
Mariatti
spellingShingle Fatriansyah
Jaka Fajar; Linuwih
Baiq Diffa Pakarti; Andreano
Yossi; Sari
Intan Septia; Federico
Andreas; Anis
Muhammad; Surip
Siti Norasmah; Jaafar
Mariatti
Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
Polymer Science
author_facet Fatriansyah
Jaka Fajar; Linuwih
Baiq Diffa Pakarti; Andreano
Yossi; Sari
Intan Septia; Federico
Andreas; Anis
Muhammad; Surip
Siti Norasmah; Jaafar
Mariatti
author_sort Fatriansyah
spelling Fatriansyah, Jaka Fajar; Linuwih, Baiq Diffa Pakarti; Andreano, Yossi; Sari, Intan Septia; Federico, Andreas; Anis, Muhammad; Surip, Siti Norasmah; Jaafar, Mariatti
Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
POLYMERS
English
Article
Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (Tg) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring Tg, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict Tg efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R2 value of 0.79; however, the XGB model, with an R2 value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for Tg prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model's robustness, with an average prediction deviation of 9.76 from actual Tg values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials.
MDPI

2073-4360
2024
16
17
10.3390/polym16172464
Polymer Science
gold
WOS:001311422500001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001311422500001
title Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
title_short Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
title_full Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
title_fullStr Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
title_full_unstemmed Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
title_sort Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
container_title POLYMERS
language English
format Article
description Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (Tg) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring Tg, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict Tg efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R2 value of 0.79; however, the XGB model, with an R2 value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for Tg prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model's robustness, with an average prediction deviation of 9.76 from actual Tg values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials.
publisher MDPI
issn
2073-4360
publishDate 2024
container_volume 16
container_issue 17
doi_str_mv 10.3390/polym16172464
topic Polymer Science
topic_facet Polymer Science
accesstype gold
id WOS:001311422500001
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001311422500001
record_format wos
collection Web of Science (WoS)
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