# Understanding Polymers Through Transfer Learning and Explainable AI
## 3 Results and Discussion
### Comparison of Both Approaches  
The differences in performance between the direct modelling and transfer learning-based approaches can be attributed to the inherent limitations of molecular-based model fingerprinting. To fully understand these limitations, it is crucial to consider the underlying physics of the glass transition processes in linear polymers, particularly polyacrylates. In these polymers, the glass transition can be rationalized as a chain relaxation within a characteristic length, the Kuhn length [43; 44]. Within this characteristic region, which for example is approximately 1.7 nm for PMMA [43], several factors like segment stiffness (in turn influenced by the chemical groups present), covalent bonding, entanglements with neighbouring chains, and intermolecular forces, play critical roles in determining the glass transition temperature [43]. Figure 7a presents a schematic representation of these phenomena, where chemical groups are represented by balls connected by covalent bonds. The dashed circles indicate the characteristic length within which relaxation occurs (in turn illustrated by a red arrow), and the dashed blue lines represent the intermolecular forces at play. The neural network must implicitly learn the contributions of these various factors, which could be summarized as: (a) the chemical structure as encoded by the SMILES strings, (b) the stiffness, intramolecular forces and entanglement effects of the polymer chains, and (c) the intermolecular forces at play. In contrast, the glass transition process in molecular systems is not confined to a single polymer chain but occurs across multiple molecules, forming a sort of physical network (Figure 7b). As a result, chain stiffness and entanglements play a less significant role in these systems [43], and pre-trained models (which are based on molecular data) capture less information about these phenomena. Consequently, these aspects must be learned during the transfer learning process, provided that the fingerprint data allows for it. The puzzle in Figure 7c schematically represents the complete physics of the system (in this case the Tg in atactic polyacrylates above molecular weight saturation), with the missing piece representing the irreducible error (due to factors such as limited data, variations in measurement techniques, and errors in sample preparation) and the missing physical phenomena in the fingerprint (e.g., the above-mentioned polymer chain effects). Given this interpretation, the superior performance of the direct model can be explained by its ability to directly learn the relevant chemical features from the dataset of polyacrylate monomers. In this particular example, and although both models show good performance, this diminishes the relative advantage of using a transfer learning approach but provides an excellent example of the underlying physics' importance. Figure 7: **(a)** A schematic of the glass transition process in polyacrylates, showing the relaxation of polymer chains within a characteristic length (Kuhn length, represented by dashed circles) where several factors, such as segment stiffness, covalent bonds, entanglements, and intermolecular forces, play crucial roles. (b) A schematic of the glass transition process in molecular systems, where the transition occurs across multiple molecules, with less emphasis on chain stiffness and entanglements. (c) The puzzle analogy representing the complete physics of the glass transition process, with the missing piece indicating irreducible error and missing physical phenomena in the molecular-based fingerprint. While deviation metrics provide a quantitative measure of performance, they offer only a superficial understanding of the models' ability to generalize across different scenarios. Even comparisons based on training robustness during hyperparameter tuning do not fully explain the differences in the models' ability to generalize.