Machine learning of optical properties of materials – predicting spectra from images and images from spectra

Stein, Helge S.; Guevarra, Dan; Newhouse, Paul F.; Soedarmadji, Edwin; Gregoire, John M.

doi:10.1039/C8SC03077D

Title: Machine learning of optical properties of materials – predicting spectra from images and images from spectra

Journal Article · 31 December 2018 · Chemical Science

DOI: https://doi.org/10.1039/C8SC03077D · OSTI ID:1480874

^[1]; Guevarra, Dan ^[1]; Newhouse, Paul F. ^[1]; Soedarmadji, Edwin ^[1];

^[1]

Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA

Assembling the world's largest materials image and spectroscopy dataset enables training of machine learning models that learn hidden relationships in materials data, providing a key example of the data requirements to capitalize on recent advancements in computer science.

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Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: SC0004993

OSTI ID:: 1480874

Journal Information:: Chemical Science, Journal Name: Chemical Science Journal Issue: 1 Vol. 10; ISSN 2041-6520; ISSN CSHCBM

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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