# Understanding Polymers Through Transfer Learning and Explainable AI
## 3 Results and Discussion
### Comparison of Both Approaches
Figure 8: Comparison of Shapley value contributions between the direct and transfer learning-based models for samples with the largest deviations. The figure highlights the weighting of specific chemical groups, such as tert-butyl and methacrylate groups, where the transfer learning model shows more heterogeneous behaviour compared to the direct model. The results suggest that the transfer learning model underestimates the influence of short nonpolar chains, leading to poorer generalization to polymer chains. Figure 9: Shapley value contributions for an example where both the direct and transfer learning models slightly overestimate Tg. The analysis shows how the linear segment within the side chain (A) decreases the predicted Tg, while stiffer structures, such as phenyl groups, increase it (B). The direct model exhibits a better understanding of the impact of side chain structures on Tg, particularly when these structures significantly influence the polymer’s thermal behaviour. A comparison of results of the direct and transfer learning models is shown in Table 1. Shapley value analysis provides a valuable complementary perspective on how different chemical features influence Tg, offering insights beyond simple numerical correlation. This level of interpretability is crucial for validating the models' predictions and understanding the limitations of the transfer learning methods, especially under data scarcity conditions. The analysis not only enhances the models' interpretability but also offers critical insights into the molecular features that govern Tg. Moving forward, the perspectives provided by this study open the door to explore other critical polymer properties beyond Tg. While experimental validation remains necessary, these insights can serve as a valuable tool for selecting promising candidates for further testing. This would enable the rational design of multifunctional, next-generation materials optimized for advanced industrial applications, including sustainable energy solutions and green technologies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Direct Model** & **Transfer Learning Model** \\ \hline Training data & Trained directly on polymer-specific dataset. & Pre-trained on molecular dataset and fine-tuned on polymer dataset. Performs well even with small dataset due to transfer of pre-trained knowledge. \\ Prediction & Higher accuracy for polymer-specific phenomena. & Comparable accuracy, but lower for certain polymer-specific interactions. It shows a biased output for short side chain samples. \\  & Limited to polycarylates (or chemically similar) data; struggles in data scarcity conditions. & Pre-trained on molecular dataset and fine-tuned on polymer dataset. Performs well even with small dataset due to transfer of pre-trained knowledge. \\ Handling of Chain & Effectively captures polymer chain stiffness, entanglements, and intra-chain effects. \\ Interpretability & Shapley values indicate consistent contributions. & Shows heterogeneous Shapley contributions for short pendant chains. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of results of the direct and transfer learning models. Figure 10: Shapley value contributions for two examples with complex side chains where the transfer learning model shows low deviations. The phenyl group and oxygen-containing segments (B) are shown to increase the predicted Tg due to their stiffness and influence on dipolar forces. The analysis highlights the transfer learning model’s ability to accurately predict Tg in polymers with more complex side chain structures, despite its general limitations.