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Title:

Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics

Document type:
Zeitschriftenaufsatz
Author(s):
Stanley, J.C.; Mayr, F.; Gagliardi, A.
Abstract:
Compositional engineering of perovskites has enabled the precise control of material properties required for their envisioned applications in photovoltaics. However, challenges remain to address efficiency, stability, and toxicity simultaneously. Mixed lead‐free and inorganic perovskites have recently demonstrated potential for resolving such issues but their composition space is gigantic, making it difficult to discover promising candidates even using high‐throughput methods. A machine learning...     »
Keywords:
density functional theory feature engineering lead‐free perovskites machine learning materials prediction
Journal title:
Adv. Theory Simul. 1900178 2019-11
Year:
2019
Year / month:
2019-11
Quarter:
4. Quartal
Month:
Nov
Pages contribution:
1-6
Language:
en
Fulltext / DOI:
doi:10.1002/adts.201900178
WWW:
https://onlinelibrary.wiley.com/doi/full/10.1002/adts.201900178
Publisher:
Wiley Online Library
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