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. Our worldwide conferences cover cutting-edge materials topics like perovskite solar cells, photovoltaics, optoelectronics, solar fuel conversion, surface science, catalysis and two-dimensional materials, among many others.nanoGe Fall MeetingnanoGe Fall Meeting (NFM) is a multiple symposia conference celebrated yearly and focused on a broad set of topics of advanced materials preparation, their fundamental properties, and their applications, in fields such as renewable energy, photovoltaics, lighting, semiconductor quantum dots, 2-D materials synthesis, charge carriers dynamics, microscopy and spectroscopy semiconductors fundamentals, etc.nanoGe Spring MeetingThis conference is a unique series of symposia focused on advanced materials preparation and fundamental properties and their applications, in fields such as renewable energy (photovoltaics, batteries), lighting, semiconductor quantum dots, 2-D materials synthesis and semiconductors fundamentals, bioimaging, etc.International Conference on Hybrid and Organic PhotovoltaicsInternational Conference on Hybrid and Organic Photovoltaics (HOPV) is celebrated yearly in May. The main topics are the development, function and modeling of materials and devices for hybrid and organic solar cells. The field is now dominated by perovskite solar cells but also other hybrid technologies, as organic solar cells, quantum dot solar cells, and dye-sensitized solar cells and their integration into devices for photoelectrochemical solar fuel production.Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and OptoelectronicsThe main topics of the Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and Optoelectronics (IPEROP) are discussed every year in Asia-Pacific for gathering the recent advances in the fields of material preparation, modeling and fabrication of perovskite and hybrid and organic materials. Photovoltaic devices are analyzed from fundamental physics and materials properties to a broad set of applications. The conference also covers the developments of perovskite optoelectronics, including light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling.International Conference on Perovskite Thin Film Photovoltaics Perovskite Photonics and OptoelectronicsThe International Conference on Perovskite Thin Film Photovoltaics Perovskite Photonics and Optoelectronics (NIPHO) is the best place to hear the latest developments in perovskite solar cells as well as on recent advances in the fields of perovskite light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.