Machine Learning Identification of Organic Compounds Using Visible Light

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols and applications.

Automated summary

Identifying chemical compounds is crucial in various scientific and engineering fields. While laser-based techniques have shown promise, optical identification using visible light has not been achieved. In this study, a machine learning classifier is developed based on decades of experimental refractive index data, allowing accurate identification of organic species using a single-wavelength dispersive measurement in the visible spectral region. The proposed optical classifier can be applied to autonomous material identification protocols and applications.