Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples
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 foundation model for IR spectroscopy with more than 100 million parameters that enables simulation-to-real transfer learning for chemical sensing and analysis from molecules to complex samples. UltraIR is...
| Authors | Yusen Tan, Yixuan Chen, Zheng Fang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 August 2026 |
| Last updated at source | 14 August 2026 |
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