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Reconstructing chromatic dispersion relations and predicting refractive indices using text mining and machine learning

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Cole, Jacqueline 
Zhao, Jiuyang 


Predicting the properties of materials prior to their syntheses is of great significance in materials science. Optical materials exhibit a large number of interesting properties that make them useful in a wide range of applications, including optical glasses, optical fibers, and laser optics. In all these applications, refraction and its chromatic dispersion can directly reflect characteristics of the transmitted light and determine the practical feasibility of the material. We demonstrate the feasibility of reconstructing chromatic dispersion relations of well-known optical materials, by aggregating data over a large number of independent sources, which are contained within a materials database of experimentally determined refractive indices and wavelength values. We also employ this database to develop a machine- learning platform that can predict refractive indices of compounds without needing to know the structure or other properties of a material of interest. We present a web-based application that enables users to build their customized machine-learning models; this will help the scientific community to conduct further research into optical materials discovery.



Data Mining, Light, Machine Learning, Refraction, Ocular, Refractometry

Journal Title

Journal of Chemical Information and Modeling

Conference Name

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Volume Title


American Chemical Society
Royal Academy of Engineering (RAEng) (RCSRF1819\7\10)
STFC (Unknown)
BASF, Royal Academy of Engineering, STFC, Cambridge Trust, and the China Scholarship Council.