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Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate

Zhao, Y. 1; Schiffmann, N. 2; Koeppe, A. ORCID iD icon 1; Brandt, N. ORCID iD icon 1; Bucharsky, E. C. 2; Schell, K. G. ORCID iD icon 2; Selzer, M. 1; Nestler, B. 1
1 Institut für Angewandte Materialien – Computational Materials Science (IAM-CMS), Karlsruher Institut für Technologie (KIT)
2 Institut für Angewandte Materialien – Keramische Werkstoffe und Technologien (IAM-KWT1), Karlsruher Institut für Technologie (KIT)

Abstract:

Lithium-ion batteries with solid electrolytes offer safety, higher energy density and higher long-term performance, which are promising alternatives to conventional liquid electrolyte batteries. Lithium aluminum titanium phosphate (LATP) is one potential solid electrolyte candidate due to its high Li-ion conductivity. To evaluate its performance, influences of the experimental factors on the materials design need to be investigated systematically. In this work, a materials design strategy based on machine learning (ML) is employed to design experimental conditions for the synthesis of LATP. In the variation of parameters, we focus on the tolerance against the possible deviations in the concentration of the precursors, as well as the influence of sintering temperature and holding time. Specifically, models built with different design selection strategies are compared based on the training data assembled from previous laboratory experiments. The best one is then chosen to design new experiment parameters, followed by measuring the corresponding properties of the newly synthesized samples. A previously unknown sample with ionic conductivity of 1.09 × 10$^{-3}$ S cm$^{-1}$ is discovered within several iterations. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000143495
Veröffentlicht am 08.03.2022
Originalveröffentlichung
DOI: 10.3389/fmats.2022.821817
Scopus
Zitationen: 10
Web of Science
Zitationen: 6
Dimensions
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Materialien – Computational Materials Science (IAM-CMS)
Institut für Angewandte Materialien – Keramische Werkstoffe und Technologien (IAM-KWT1)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2296-8016
KITopen-ID: 1000143495
Erschienen in Frontiers in Materials
Verlag Frontiers Media SA
Band 9
Seiten Art.-Nr.: 821817
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Nachgewiesen in Web of Science
Dimensions
Scopus
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