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Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

Velasco, Leonardo ; Castillo, Juan S. ; Kante, Mohana V. ; Olaya, Jhon J. ; Friederich, Pascal ; Hahn, Horst (2024)
Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries.
In: Advanced Materials, 2021, 33 (43)
doi: 10.26083/tuprints-00020981
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Item Type: Article
Type of entry: Secondary publication
Title: Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries
Language: English
Date: 13 February 2024
Place of Publication: Darmstadt
Year of primary publication: 2021
Place of primary publication: Weinheim
Publisher: Wiley-VCH
Journal or Publication Title: Advanced Materials
Volume of the journal: 33
Issue Number: 43
Collation: 11 Seiten
DOI: 10.26083/tuprints-00020981
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high‐throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high‐throughput techniques. For intuitive visualization, a graphical phase–property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning‐based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.

Uncontrolled Keywords: high entropy materials, high‐throughput techniques, machine learning, materials libraries, phase diagram, virtual materials
Identification Number: Artikel-ID: 2102301
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-209813
Classification DDC: 600 Technology, medicine, applied sciences > 660 Chemical engineering
Divisions: 11 Department of Materials and Earth Sciences > Material Science > Joint Research Laboratory Nanomaterials
Date Deposited: 13 Feb 2024 13:36
Last Modified: 30 Apr 2024 11:53
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20981
PPN: 517434733
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