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An open benchmark for machine learning-based polymer property prediction — arXiv:2609.27036

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27036
License (as archived): CC0-1.0
Archived: 2026-10-08T09:12:06.803Z
SHA-256 of the archived text: 55f8836970bb4b0a72b3ed8a7b07a6da111a34315477c39dfebcfd35d17d527f
Stable URL of this page: https://metraipolis.com/documento/dc_fad63e2bf010

How to cite

An open benchmark for machine learning-based polymer property prediction — arXiv:2609.27036 — arXiv (en), https://arxiv.org/abs/2609.27036 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fad63e2bf010 (Metraipolis archive dc_fad63e2bf010, sha256 55f8836970bb4b0a72b3ed8a7b07a6da111a34315477c39dfebcfd35d17d527f)

BibTeX

@misc{dcfad63e2bf010,
  title = {An open benchmark for machine learning-based polymer property prediction — arXiv:2609.27036},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27036},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fad63e2bf010; sha256 55f8836970bb4b0a72b3ed8a7b07a6da111a34315477c39dfebcfd35d17d527f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27036. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-24 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.27036 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T08:13:26.611Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).