A glimpse inside materials: Polymer structure – Glass transition temperature relationship as observed by a trained artificial intelligence

Miccio, Luis A.; Borredon, Claudia; Schwartz, Gustavo A.

doi:10.1016/j.commatsci.2024.112863

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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Section: Introductionmentioning

confidence: 99%

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

Uddin,

Fan

2024

Polymers

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Visual analytics of an interpretable prediction model for the glass transition temperature of fluoroelastomers

Liu,

Wu,

Lin

et al. 2024

Materials Today Communications

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Understanding Polymers Through Transfer Learning and Explainable AI

Miccio

2024

Applied Sciences

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In this work we study the use of artificial intelligence models, particularly focusing on transfer learning and interpretability, to predict polymer properties. Given the challenges imposed by data scarcity in polymer science, transfer learning offers a promising solution by using learnt features of models pre-trained on other datasets. We conducted a comparative analysis of direct modelling and transfer learning-based approaches using a polyacrylates’ glass transitions dataset as a proof-of-concept study. The AI models utilized tokenized SMILES strings to represent polymer structures, with convolutional neural networks processing these representations to predict Tg. To enhance model interpretability, Shapley value analysis was employed to assess the contribution of specific chemical groups to the predictions. The results indicate that while transfer learning provides robust predictive capabilities, direct modelling on polymer-specific data offers superior performance, particularly in capturing the complex interactions influencing Tg. This work highlights the importance of model interpretability and the limitations of applying molecular-level models to polymer systems.

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A glimpse inside materials: Polymer structure – Glass transition temperature relationship as observed by a trained artificial intelligence

Cited by 4 publications

References 42 publications

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

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

Visual analytics of an interpretable prediction model for the glass transition temperature of fluoroelastomers

Understanding Polymers Through Transfer Learning and Explainable AI

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