PolyMon: A Unified Framework for Polymer Property Prediction

Abstract

Accurate prediction of polymer properties is essential for materials design, but remains challenging due to data scarcity, diverse polymer representations, and the lack of systematic evaluation across modelling choices. Here, we present PolyMon, a unified and accessible framework that integrates multiple polymer representations, machine learning methods, and training strategies within a single, accessible platform. PolyMon supports various descriptors and graph construction strategies for polymer representations, and includes a wide range of models, from tabular models to graph neural networks, along with flexible training strategies including multi-fidelity learning, Δ-learning, active learning, and ensemble learning. Using five key polymer properties as benchmarks, we perform systematic evaluations to assess how representations and models affect predictive performance. These case studies further illustrate how different training strategies can be applied within a consistent workflow to leverage limited data and incorporate physical model derived information. Overall, PolyMon provides a comprehensive and extensible foundation for benchmarking and advancing machine learning-based polymer property prediction. The code is available at github.com/fate1997/polymon.

Figures (4)

• Figure 1: An overview of the PolyMon framework. The framework supports multiple polymer representations, modelling approaches, and training strategies for polymer property prediction. The PolyMon logo was designed with assistance from Gemini.
• Figure 2: Polymer graphs. Purple nodes denote atoms, blue nodes represent the neighbours of attachment points, and the red node indicates a virtual node. Black edges correspond to chemical bonds, red edges denote additional connections between attachment points, and dashed edges represent virtual connections between atoms and the virtual node.
• Figure 3: Performance comparison between tabular models (TabPFN and LightGBM) and GNNs (PNA and GPS) on five polymer properties.
• Figure 4: Performance of different training strategies. (a) Multi-fidelity learning on the experimental density dataset ($\rho_{\rm exp}$); baseline: GATv2 trained directly on $\rho_{\rm exp}$. finetune (freeze/all): pretrained on $\rho_{\rm md}$ then finetuned on $\rho_{\rm exp}$, with frozen or fully trainable parameters. residual (label/emb): residual learning on labels or graph embeddings. (b) Property knowledge transfer: each row shows the target property using embeddings from the column property. Diagonal: baseline; red: improved, blue: worse. (c) $\Delta$-learning using empirical equations ($\rho_{\rm md}$, $R_{g}$) or atomic contributions TC, $T_{g}$, FFV). (d) Active learning: "Random" selects points randomly; "Uncertainty" selects based on uncertainty. (e) Ensemble performance with varying numbers of estimators.