2023-Sustainable Industrial Processing Summit
SIPS2023 Volume 8. Poeppelmeier Intl. Symp. / Solid State Chemistry
| Editors: | F. Kongoli, M. Delferro, P. S. Halasyamani, M. A. Alario-Franco, F. Marquis, A. Tressaud, H. Kageyama |
| Publisher: | Flogen Star OUTREACH |
| Publication Year: | 2023 |
| Pages: | 144 pages |
| ISBN: | 978-1-989820-86-5 (CD) |
| ISSN: | 2291-1227 (Metals and Materials Processing in a Clean Environment Series) |
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IDENTIFICATION OF NEW HYBRID PEROVSKITES FROM X‐RAY DIFFRACTION PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS
Romain Gautier1; Florian Massuyeau2;1CNRS UMR6502 IMN, Nantes, France; 2CNRS - IMN, NANTES, France;
Type of Paper: Regular
Id Paper: 262
Topic: 52
Abstract:
Hybrid lead halides of perovskite type have recently shown a great potential in optoelectronic applications. For this reason, many research groups are currently exploring this chemical system to discover new low dimensional hybrid perovskites. However, discovering such materials is challenging as the necessary structure determination by X-ray crystallography is time consuming and non-perovskite compounds are very often synthesized instead of perovskites.
In this context, we developed a deep learning approach, which automatically and accurately assign the structure type from the X-ray diffraction patterns of new hybrid lead halides [1]. The models could automatically identify new hybrid perovskites with an accuracy of 92%. Interestingly, we were able to identify and explain the key features in the diffraction patterns, which allow the machine learning algorithms to discriminate between perovskites and non perovskites. From this information, the scientists’ ability in discriminating the different structure types is augmented and such algorithms could be included in autonomous materials discovery cycles in the future.