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Title:

Machine Learning Bandgaps of Inorganic Mixed Halide Perovskites

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Stanley, J.; Gagliardi, A.
Abstract:
The identification of suitable lead-free perovskites is crucial for their envisioned applications in photovoltaics. Efficient and accurate vetting of these compounds for a range of properties has recently been accomplished in high-throughput studies by use of statistical learning methods. Here we demonstrate how one such property, the fundamental bandgap, can be predicted for a family of inorganic mixed halide perovskites using fingerprints based solely on the atomic arrangement of the unit cell...     »
Book / Congress title:
IEEE 18th International Conference on Nanotechnology (IEEE-NANO)
Congress (additional information):
Cork, Ireland, 23-26 July 2018-07
Publisher:
IEEE Xplore Digital Library
Year:
2018
Quarter:
3. Quartal
Year / month:
2018-07
Month:
Jul
Print-ISBN:
978-1-5386-5337-1
E-ISBN:
978-1-5386-5336-4
Bookseries ISSN:
1944-9380 1944-9399
Language:
en
Fulltext / DOI:
doi:10.1109/NANO.2018.8626420
WWW:
https://ieeexplore.ieee.org/document/8626420
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