TY - GEN
T1 - Machine Learning Bandgaps of Inorganic Mixed Halide Perovskites
AU - Stanley, J.
AU - Gagliardi, A.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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. Important trends and experimentally accessible factors controlling this property are thereby illuminated in a chemically intuitive manner.
AB - 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. Important trends and experimentally accessible factors controlling this property are thereby illuminated in a chemically intuitive manner.
UR - http://www.scopus.com/inward/record.url?scp=85062288113&partnerID=8YFLogxK
U2 - 10.1109/NANO.2018.8626420
DO - 10.1109/NANO.2018.8626420
M3 - Conference contribution
AN - SCOPUS:85062288113
T3 - Proceedings of the IEEE Conference on Nanotechnology
BT - 18th International Conference on Nanotechnology, NANO 2018
PB - IEEE Computer Society
T2 - 18th International Conference on Nanotechnology, NANO 2018
Y2 - 23 July 2018 through 26 July 2018
ER -