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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202203879
|2 doi
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|a eng
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1 |
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|a Massuyeau, Florian
|e verfasserin
|4 aut
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|a Perovskite or Not Perovskite? A Deep-Learning Approach to Automatically Identify New Hybrid Perovskites from X-ray Diffraction Patterns
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 14.10.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Wiley-VCH GmbH.
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|a 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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|a Journal Article
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|a X-ray diffraction
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|a deep learning
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|a hybrid perovskites
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| 700 |
1 |
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|a Broux, Thibault
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Coulet, Florent
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Demessence, Aude
|e verfasserin
|4 aut
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|a Mesbah, Adel
|e verfasserin
|4 aut
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|a Gautier, Romain
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 41 vom: 10. Okt., Seite e2203879
|w (DE-627)NLM098206397
|x 1521-4095
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|g volume:34
|g year:2022
|g number:41
|g day:10
|g month:10
|g pages:e2203879
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|u http://dx.doi.org/10.1002/adma.202203879
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