# Understanding Polymers Through Transfer Learning and Explainable AI
###### Abstract  
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. AI; white boxing; transfer learning; polymer glass transition Article Understanding Polymers Through Transfer Learning and Explainable AI  
L.A. Understanding Polymers Through Transfer Learning and Explainable AI  
1 Donostia International Physics Center, P. M. de Lardizabal 4, 20018 San Sebastian, Spain; 1 [EMAIL_ADDRESS] 1 [EMAIL_ADDRESS] 1 luisalejadmorcio@ehu. One of the major challenges in applying AI to materials science, and particularly to polymer science, is data scarcity. High-quality, experimentally validated datasets are often limited, particularly in very specialized or niche domains. This scarcity can hinder the ability of AI models to learn and generalize effectively to new data, therefore leading to inaccurate predictions or even complete failures in capturing the underlying physics of a problem. To mitigate this issue, transfer learning has emerged as a powerful tool [25]. By fine-tuning pre-trained models on large, related datasets, transfer learning enables the adaptation of AI models to new tasks by using relatively small amounts of data, thus improving prediction accuracy and model robustness. This approach has shown great promise in fields where data collection is difficult, costly or very time-consuming, providing a viable path forward in the face of limited data [26]. For example, the prediction of the glass transition temperature (Tg) of polymers is a complex task that exemplifies the challenges in the application of AI to materials science. Tg is a critical thermal property that accounts for the transition of a polymer from a rigid, glassy state to a more flexible, rubbery state [27; 28]. Accurate prediction of Tg is essential for the design and application of polymers in various industries, including packaging, automotive, and electronics. Traditional methods for predicting Tg often rely on empirical models that require extensive experimental data, which can be difficult to obtain for new or complex polymers. AI models offer a more flexible approach by learning the underlying relationships between polymer structure and Tg directly from data [29]. For instance, fully connected [30], convolutional [31] or graph neural networks [32], can efficiently capture structure-property relationships. In this study, we focus on investigating the limitations of transfer learning when applied to the specific problem of predicting properties of polymers. In particular, we utilize models pre-trained on datasets of glass transition temperatures of pure molecules and apply them to the more complex task of predicting the Tg of long polymer chains. While transfer learning offers a pathway to overcome data scarcity, the difference in the fundamental physics of the problems presents unique challenges that may not be fully addressed by models trained on simpler molecular structures.