{
  "id": 19169,
  "url": "https://arxiv.org/abs/2608.13341v1",
  "title": "Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples",
  "summary": "Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets. Here we introduce UltraIR, a foundat",
  "authors": "Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo et al.",
  "category": "research",
  "topics": "jobs-economy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T15:11:50.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19169",
  "original_url": "https://arxiv.org/abs/2608.13341v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}