Journal
ADVANCED MATERIALS
Volume 34, Issue 41, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202203879
Keywords
deep learning; hybrid perovskites; X-ray diffraction
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Funding
- National Agency for Research [ANR-16-CE08-0003-01, ANR-21-ERCC-0009-01]
- Region Pays de la Loire (Etoiles montantes en Pays de la Loire 2017, project Decouverte de perovskites hybrides assistee par ordinateur)
- Agence Nationale de la Recherche (ANR) [ANR-16-CE08-0003, ANR-21-ERCC-0009] Funding Source: Agence Nationale de la Recherche (ANR)
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This article introduces a machine learning-based method that utilizes X-ray diffraction patterns to automatically determine whether unknown materials are perovskite-type materials, providing a new efficient approach for determining crystal structures.
Determining the crystal structure is a critical step in the discovery of new functional materials. This process is time consuming and requires extensive human expertise in crystallography. Here, a machine-learning-based approach is developed, which allows it to be determined automatically if an unknown material is of perovskite type from powder X-ray diffraction. After training a deep-learning model on a dataset of known compounds, the structure types of new unknown compounds can be predicted using their experimental powder X-ray diffraction patterns. This strategy is used to distinguish perovskite-type materials in a series of new hybrid lead halides. After validation, this approach is shown to accurately identify perovskites (accuracy of 92% with convolutional neural network). From the identification of the key features of the patterns used to discriminate perovskites versus nonperovskites, crystallographers can learn how to quickly identify low-dimensional perovskites from X-ray diffraction patterns.
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