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Accelerating Materials Discovery: Automated Identification of Prospects from X‐Ray Diffraction Data in Fast Screening Experiments

Schuetzke, Jan ORCID iD icon 1; Schweidler, Simon ORCID iD icon 2; Muenke, Friedrich R. ORCID iD icon 1; Orth, Andre 1; Khandelwal, Anurag D. 2; Breitung, Ben ORCID iD icon 2; Aghassi-Hagmann, Jasmin ORCID iD icon 2; Reischl, Markus ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)
2 Institut für Nanotechnologie (INT), Karlsruher Institut für Technologie (KIT)

Abstract:

New materials are frequently synthesized and optimized with the explicit intention to improve their properties to meet the ever-increasing societal requirements for high-performance and energy-efficient electronics, new battery concepts, better recyclability, and low-energy manufacturing processes. This often involves exploring vast combinations of stoichiometries and compositions, a process made more efficient by high-throughput robotic platforms. Nonetheless, subsequent analytical methods are essential to screen the numerous samples and identify promising material candidates. X-ray diffraction is a commonly used analysis method available in most laboratories which gives insight into the crystalline structure and reveals the presence of phases in a powder sample. Herein, a method for automating the analysis of XRD patterns, which uses a neural network model to classify samples into nondiffracting, single-phase, and multi-phase structures, is presented. To train neural networks for identifying materials with compositions not matching known crystallographic structures, a synthetic data generation approach is developed. The application of the neural networks on high-entropy oxides experimental data is demonstrated, where materials frequently deviate from anticipated structures. ... mehr

Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Institut für Nanotechnologie (INT)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 01.01.2024
Sprache Englisch
Identifikator ISSN: 2640-4567
KITopen-ID: 1000166061
HGF-Programm 43.31.02 (POF IV, LK 01) Devices and Applications
Erschienen in Advanced Intelligent Systems
Verlag Wiley-VCH Verlag
Band 6
Heft 3
Seiten Art.-Nr.: 2300501
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 24.12.2023
Nachgewiesen in Dimensions
OpenAlex
Web of Science
Scopus
Globale Ziele für nachhaltige Entwicklung Ziel 7 – Bezahlbare und saubere EnergieZiel 9 – Industrie, Innovation und Infrastruktur

Verlagsausgabe §
DOI: 10.5445/IR/1000166061
Veröffentlicht am 29.12.2023
Originalveröffentlichung
DOI: 10.1002/aisy.202300501
Scopus
Zitationen: 6
Web of Science
Zitationen: 4
Dimensions
Zitationen: 4
Seitenaufrufe: 176
seit 30.12.2023
Downloads: 95
seit 08.01.2024
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