Reconstructing chromatic dispersion relations and predicting refractive indices using text mining and machine learning
Journal of Chemical Information and Modeling
American Chemical Society
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Cole, J., & Zhao, J. (2022). Reconstructing chromatic dispersion relations and predicting refractive indices using text mining and machine learning. Journal of Chemical Information and Modeling https://doi.org/10.1021/acs.jcim.2c00253
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.
BASF, Royal Academy of Engineering, STFC, Cambridge Trust, and the China Scholarship Council.
Royal Academy of Engineering (RAEng) (RCSRF1819\7\10)
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External DOI: https://doi.org/10.1021/acs.jcim.2c00253
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336629
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