Machine learning techniques have emerged as an unprecedented tool to identify complex patterns in large datasets. To explore their potential in heterogeneous catalysis research, we have combined high-throughput simulations and experiments to investigate promising catalysts for electrolytic oxygen production and reduction. In particular, we have used machine learning to establish trends between the structural properties<br/>and the electrochemical performance of perovskite lanthanum-based oxides. Perovskite lanthanum-based oxides are promising materials for solid oxide fuel cells because of their improved oxygen transport properties and resistance to the high operating temperatures. Based on experimental data from combinatorial ternary and quaternary maps, we have derived structure-property-performance relationships that could lead to the design of better lanthanum-based materials.

Machine-learning-guided design of perovskite lanthanum oxide catalysts for solid oxide fuel cells

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ACS Spring 2023

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