An open benchmark for machine learning-based polymer property prediction

The article introduces PolyBench26, an open benchmark dataset for polymer property prediction that contains nearly 250,000 datapoints covering eight physical properties from experimental, DFT, and MD sources. It supports four evaluation tasks—property prediction, dataset-size scaling, repeat‑unit complexity, and transfer learning—across homopolymers and various copolymer architectures. The study compares language, graph, and descriptor models, finding graph-based approaches achieve the lowest errors and maintain robustness across training sizes and repeat‑unit complexity.

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• llms
• benchmarks