Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
DOI: https://doi.org/10.29363/nanoge.matsus.2024.250
Publication date: 18th December 2023
Lanthanum strontium-based perovskites (ABO3) are among the state-of-the-art cathode materials for solid oxide fuel cell operating at intermediate and low temperatures (<800 ºC).(1,2) However, the effects of the composition on the nanostructure and the intrinsic properties of the materials and on the electrochemical performance are typically non-linear and hard to generalize.(3,4) Machine learning techniques have emerged as an unprecedented tool to identify complex patterns in large datasets, also in heterogeneous electrocatalysis (5,6). Herein, we have applied these techniques to delve deeper in the composition-property-performance relationships of La0.8Sr0.2(Mn,Co,Fe)O3±𝞭 and predict performance maps that can help optimize these materials. High-throughput characterization of a compositional map of La0.8Sr0.2(Mn,Co,Fe)O3±𝞭 has been carried out: information on the metal stoichiometry, the crystallinity, electrochemical performance, the structural symmetry, and the electronic configuration was obtained from X-ray diffraction (XRD), X-ray fluorescence (XRF), electrochemical impedance spectroscopy (EIS), Raman spectroscopy and ellipsometry, respectively. We processed the raw data to derive characteristic features and match the samples from different measurements. Then, a variety of supervised and unsupervised modern machine learning methods were utilized to build highly generalizable models correlating experimental features relative to the composition, the optical properties and the electrochemical pperformance of the materials, and to identify the most relevant ones. Experimental data from Raman and ellipsometry and XRD measurements was demonstrated to model the material composition and the electrochemical performance with R2 of 0.913 ± 0.002 and 0.900 ± 0.003, and mean absolute errors of 0.053 ± 0.001 and 0.189 ± 0.005, respectively, with 5-fold cross-validation.
C. B.-G. acknowledges funding from a Marie Skłodowska Curie Actions Postdoctoral Fellowship grant (101064374)
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