Prediction of oxygen reduction performance of quaternary perovskites La0.8Sr0.2(Co,Fe,Mn)O3 with machine learning based on spectroscopic characterization data

Prediction of oxygen reduction performance of quaternary perovskites La0.8Sr0.2(Co,Fe,Mn)O3 with machine learning based on spectroscopic characterization dataCarlota Bozal-Ginesta a, b, Juande Sirvent a, Sergio Pablo-García b, Francesco Chiabrera a, Changhyeok Choi b, Lisa Laa a, Federico Baiutti a, Alex Morata a, Alán Aspuru-Guzik b, Albert Tarancón aa Nanoionics and Fuel Cells group, Catalonia Institute for Energy Research, Jardins de Les Dones de Negre 1, 08930 Sant Adrià de Besòs, Barcelona, Spainb Departments of Chemistry and Computer Science, University of Toronto, Lash Miller Chemical Laboratories, 80 St George Street, Toronto, ON M5S 3H6, CanadaMaterials for Sustainable Development Conference (MATSUS)Proceedings of MATSUS Spring 2024 Conference (MATSUS24)#AI - Automation and Nanomaterials (machine learning, artificial intelligence, robotics, accelerated discovery)Barcelona, Spain, 2024 March 4th - 8thOrganizers: Ivan Infante and Oleksandr VoznyyOral, Carlota Bozal-Ginesta, presentation 250DOI: https://doi.org/10.29363/nanoge.matsus.2024.250Publication date: 18th December 2023Lanthanum 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. References:[1] Wachsman, E. D.; Lee, K. T. Lowering the Temperature of Solid Oxide Fuel Cells. Science 2011, 334 (6058), 935–939[2] Skinner, S. J. Recent Advances in Perovskite-Type Materials for Solid Oxide Fuel Cell Cathodes. International Journal of Inorganic Materials 2001, 3 (2), 113–121[3] Saranya, A. M.; Pla, D.; Morata, A.; Cavallaro, A.; Canales-Vázquez, J.; Kilner, J. A.; Burriel, M.; Tarancón, A. Engineering Mixed Ionic Electronic Conduction in La0.8Sr0.2MnO3+δ Nanostructures through Fast Grain Boundary Oxygen Diffusivity. Advanced Energy Materials 2015, 5 (11), 1500377[4] Chiabrera, F.; Garbayo, I.; Lopez-Conesa, L.; Martin, G.; Ruiz-Caridad, A.; Walls, M.; Ruiz-Gonzalez, L.; Kordatos, (7) A.; Nunez, M.; Morata, A.; Estrade, S.; Chroneos, A.; Peiro, F.; Tarancon, A. Engineering Transport in Manganites by Tuning Local Nonstoichiometry in Grain Boundaries. Adv Mater 2019, 31 (4), e1805360[5] Ulissi, Z. W.; Tang, M. T.; Xiao, J.; Liu, X.; Torelli, D. A.; Karamad, M.; Cummins, K.; Hahn, C.; Lewis, N. S.; Jaramillo, T. F.; Chan, K.; Nørskov, J. K. Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction. ACS Catalysis 2017, 7 (10), 6600–6608[6] Batchelor, T. A. A.; Löffler, T.; Xiao, B.; Krysiak, O. A.; Strotkötter, V.; Pedersen, J. K.; Clausen, C. M.; Savan, A.; Li, Y.; Schuhmann, W.; Rossmeisl, J.; Ludwig, A. Complex-Solid-Solution Electrocatalyst Discovery by Computational Prediction and High-Throughput Experimentation**. Angewandte Chemie International Edition 2021, 60 (13), 6932–6937Acknowledgements:C. B.-G. acknowledges funding from a Marie Skłodowska Curie Actions Postdoctoral Fellowship grant (101064374) © FUNDACIO DE LA COMUNITAT VALENCIANA SCITOnanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. 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