The culmination of the SmartOptoelectronics Marie Curie Postdoctoral Global Fellowship has led to the publication of ‘Performance prediction of high-entropy perovskites La0.8Sr0.2MnxCoyFezO3 with automated high-throughput characterization of combinatorial libraries and machine learning’, a comprehensive investigation spearheaded by Dr. Carlota Bozal-Ginesta, Albert Tarancón’s group at IREC and Alán Aspuru-Guzik’s group at the University of Toronto, in collaboration with several institutions (IREC, University of Toronto, Imperial College London, Kyushu University and CNRS).
Perovskite oxides are celebrated for their structural and chemical flexibility, with applications across various fields. However, efficiently exploring the vast compositional space has remained a challenge. This project addressed this by combinatorial synthesis, high-throughput characterization methodologies and machine learning (ML) approaches. It investigated the composition-structure-performance relationships of La0.8Sr0.2MnxCoyFezO3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes, and successfully modelled the mass transport properties and electrochemical performance of the whole material family.
The completion of this fellowship marks a pivotal step towards a more efficient and cost-effective development of high-performance materials for clean energy technologies such as solid oxide fuel cells (SOFCs).
Reference: Bozal-Ginesta, C., Sirvent, J., Cordaro, G., Fearn, S., Pablo-García, S., Chiabrera, F., Choi, C., Laa, L., Núñez, M, Cavallaro, A., Buzi, F., Aguadero, A., Dezanneau, G., Kilner, J., Morata, A., Baiutti, F., Aspuru-Guzik, A., Tarancón, A., Performance prediction of high-entropy perovskites La0.8Sr0.2MnxCoyFezO3 with automated high-throughput characterization of combinatorial libraries and machine learning, ChemRxiv, 2024
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.
