An open benchmark for machine learning-based polymer property prediction

Researchers introduced Polymer Benchmark 2026 (PolyBench26), an open dataset of nearly 250,000 polymer-property datapoints spanning eight physical properties and four evaluation tasks across homopolymers and alternating, random, and block copolymers. Comparing language model, graph-based, and descriptor-based approaches, the team found graph-based models achieve the lowest property-prediction errors, retain that advantage across evaluated training-set sizes, and remain robust as repeat-unit complexity increases. The benchmark is available open-source at https://github.com/rlearsch/PolymerBenchmark2026.

arXiv:2609.27036v1 Announce Type: new Abstract: Polymer property prediction lacks open, standardized benchmarks that enable rigorous comparison of machine-learning methods, with existing resources covering only a narrow fraction of polymer architectures, such as homopolymers. We introduce Polymer Benchmark 2026 PolyBench26 , an open dataset comprising nearly 250,000 polymer-property datapoints across eight physical properties, including data from experimental measurements, density functional theory, and molecular dynamics. The benchmark supports four evaluation tasks across homopolymers and alternating, random, and block copolymers: in-distribution property prediction, dataset-size scaling, repeat-unit complexity, and transfer to held-out polymer architectures. We compare language model, graph-based, and descriptor-based approaches and find graph-based models provide the lowest errors in property prediction, retain their advantage across the evaluated training-set sizes, and remain robust to increasing repeat-unit complexity. PolyBench26 provides a reproducible foundation for developing models for the increasingly complex polymer design space. The PolyBench26 benchmark is available open-source at https://github.com/rlearsch/PolymerBenchmark2026.