PolyMon统一框架提升聚合物性能预测准确率
PolyMon: A Unified Framework for Polymer Property Prediction
- 整合多种聚合物表示与机器学习模型，支持多策略训练
- 在5种关键聚合物属性上系统评估不同表示与模型表现
- 适合材料设计与机器学习研究者快速搭建高效预测流程
准确预测聚合物性能对材料设计至关重要，但受限于数据稀缺、聚合物表示多样以及建模选择缺乏系统评估。本文提出PolyMon，一个统一且易用的框架，集成多种聚合物表示方式、机器学习方法与训练策略。该框架支持多种描述符与图构建方法，涵盖从表格模型到图神经网络的广泛模型，并提供多保真度学习、Δ-学习、主动学习与集成学习等灵活训练策略。以5种关键聚合物性能为基准，进行系统评估，揭示表示与模型对预测性能的影响。案例研究还展示了如何在一致工作流中应用不同训练策略，有效利用有限数据并融合物理模型信息。PolyMon为基于机器学习的聚合物性能预测提供了全面可扩展的基准平台。代码开源：github.com/fate1997/polymon。
原文摘要 · Abstract (English)
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.
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