Machine learning identification of organic compounds using visible light
This work addresses the need for remote chemical identification in science and engineering, offering a novel approach for autonomous material identification, though it is incremental as it builds on existing infrared techniques.
The researchers tackled the problem of identifying organic compounds using visible light, which had not been achieved before, by developing a machine learning classifier that accurately identifies species based on single-wavelength dispersive measurements in the visible region, achieving high accuracy as implied by the use of decades of experimental data.
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 or applications.