Machine Learning Identification of Organic Compounds Using Visible Light

Bikku, Thulasi; Fritz, Ruben A.; Colon, Yamil J.; Herrera, Felipe

Abstract

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.

Más información

Título según WOS: ID WOS:000945181800001 Not found in local WOS DB
Título de la Revista: JOURNAL OF PHYSICAL CHEMISTRY A
Volumen: 127
Número: 10
Editorial: AMER CHEMICAL SOC
Fecha de publicación: 2023
Página de inicio: 2407
Página final: 2414
DOI:

10.1021/acs.jpca.2c07955

Notas: ISI