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Issue No. 01 - Jan.-Feb. (2018 vol. 20)
ISSN: 1521-9615
pp: 84-87
Jacqueline M. Cole , University of Cambridge and Argonne National Laboratory
ABSTRACT
A new initiative in data science is using the Mira and Theta supercomputers at the US Department of Energy's Argonne National Laboratory to discover new dye materials suitable for dye-sensitized solar cells. This project aims to develop a new material-by-design methodology by using natural language processing, machine learning, and data mining in conjunction with large-scale simulation and experiments. This synergistic computational and experimental science approach will enable the discovery of new light-absorbing dye molecules, which are needed for the development of solar-powered windows that have the potential to power buildings in an entirely energy-sustainable fashion.
INDEX TERMS
data mining, design engineering, learning (artificial intelligence), natural language processing, solar cell arrays, structural engineering computing, sustainable development, windows (construction)
CITATION

J. M. Cole, "Data-Driven Molecular Engineering of Solar-Powered Windows," in Computing in Science & Engineering, vol. 20, no. 1, pp. 84-87, 2018.
doi:10.1109/MCSE.2018.011111129
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