Performance prediction of high-entropy perovskites La0.8Sr0.2MnxCoyFezO3 with automated high-throughput characterization of combinatorial libraries and machine learning
Author(s)
Type
Journal Article
Abstract
Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition–structure–performance relationships of high-entropy La0.8Sr0.2MnxCoyFezO3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion—derived from spectral analysis of Raman-active modes—and enhanced performance.
Date Issued
2024-12-12
Date Acceptance
2024-11-01
Citation
Advanced Materials, 2024, 36 (50)
ISSN
0935-9648
Publisher
Wiley
Journal / Book Title
Advanced Materials
Volume
36
Issue
50
Copyright Statement
© 2024 The Author(s). Advanced Materials published by Wiley-VCH GmbH This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39498698
Subjects
CATHODE
Chemistry
Chemistry, Multidisciplinary
Chemistry, Physical
DIFFUSION
ELECTROCATALYSTS
EVOLUTION
high entropy oxides
high-throughput experimentation
INTERFACE
machine learning
MANGANITE
MASS-TRANSPORT PROPERTIES
Materials Science
Materials Science, Multidisciplinary
Nanoscience & Nanotechnology
perovskite oxides
Physical Sciences
Physics
Physics, Applied
Physics, Condensed Matter
Science & Technology
Science & Technology - Other Topics
SEGREGATION
SOFC
solid oxide fuel cells
Technology
TEMPERATURE
Publication Status
Published
Coverage Spatial
Germany
Article Number
2407372
Date Publish Online
2024-11-05