Interpretation of material spectra can be data-driven using machine learning
Thursday, September 6, 2018 - 06:16
in Physics & Chemistry
Spectroscopy techniques are commonly used in materials research because they enable identification of materials from their unique spectral features. These features are correlated with specific material properties, such as their atomic configurations and chemical bond structures. Modern spectroscopy methods have enabled rapid generation of enormous numbers of material spectra, but it is necessary to interpret these spectra to gather relevant information about the material under study.