Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

Excerpt

arXiv:2608.13341v2 Announce Type: replace-cross Abstract: 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 e