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Massuyeau, F., Broux, T., Coulet, F., Demessence, A., Mesbah, A. & Gautier, R. (2022) Perovskite or Not Perovskite? A Deep-Learning Approach to Automatically Identify New Hybrid Perovskites from X-ray Diffraction Patterns. Advanced Materials, 2203879.
Added by: Richard Baschera (2022-09-29 11:52:50) Last edited by: Richard Baschera (2022-09-29 11:58:23) |
| Type de référence: Article DOI: 10.1002/adma.202203879 Numéro d'identification (ISBN etc.): 0935-9648 Clé BibTeX: Massuyeau2022 Voir tous les détails bibliographiques  |
Catégories: IMN, MIOPS Créateurs: Broux, Coulet, Demessence, Gautier, Massuyeau, Mesbah Collection: Advanced Materials |
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| Résumé |
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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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