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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 (2021)
Phase-property diagrams for multicomponent oxide systems toward materials libraries.
In: Advanced Materials, 33 (43)
doi: 10.1002/adma.202102301
Artikel, Bibliographie

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Kurzbeschreibung (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.

Typ des Eintrags: Artikel
Erschienen: 2021
Autor(en): Velasco, Leonardo ; Castillo, Juan S. ; Kante, Mohana V. ; Olaya, Jhon J. ; Friederich, Pascal ; Hahn, Horst
Art des Eintrags: Bibliographie
Titel: Phase-property diagrams for multicomponent oxide systems toward materials libraries
Sprache: Englisch
Publikationsjahr: 2021
Ort: Weinheim
Verlag: Wiley-VCH
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Advanced Materials
Jahrgang/Volume einer Zeitschrift: 33
(Heft-)Nummer: 43
Kollation: 11 Seiten
DOI: 10.1002/adma.202102301
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Kurzbeschreibung (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.

Freie Schlagworte: high entropy materials, high‐throughput techniques, machine learning, materials libraries, phase diagram, virtual materials
ID-Nummer: Artikel-ID: 2102301
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 600 Technik, Medizin, angewandte Wissenschaften > 660 Technische Chemie
Fachbereich(e)/-gebiet(e): 11 Fachbereich Material- und Geowissenschaften
11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft
11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Gemeinschaftslabor Nanomaterialien
Hinterlegungsdatum: 14 Feb 2024 07:59
Letzte Änderung: 14 Feb 2024 07:59
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