Performance Prediction of High-Entropy Perovskites La_0.8Sr_0.2Mn_xCo_yFe_zO₃ with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning.

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 La_0.8Sr_0.2Mn_xCo_yFe_zO_3±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 differen

SUBMITTER: Bozal-Ginesta C 

PROVIDER: S-EPMC11636067 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature