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 foundat

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ethics.ai (13 August 2026), “Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples,” evidence record 19169, https://ethics.ai/record/19169 (originally published by arXiv).

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