Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 32665539.
- Also identified by DOI 10.1038/s41467-020-17263-9 and PMC identifier 7360597.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs<sub>0.4</sub>La<sub>0.6</sub>Mn<sub>0.25</sub>Co<sub>0.75</sub>O<sub>3</sub>, Cs<sub>0.3</sub>La<sub>0.7</sub>NiO<sub>3</sub>, SrNi<sub>0.75</sub>Co<sub>0.25</sub>O<sub>3</sub>, and Sr<sub>0.25</sub>Ba<sub>0.75</sub>NiO<sub>3</sub>, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.