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High-Throughput Computational Screening of Cubic Perovskites for Solid Oxide Fuel Cell Cathodes
Ilker Tezsevin1,2,3, Mauritius C M van de Sanden1,3, Süleyman Er1,2
1DIFFER - Dutch Institute for Fundamental Energy Research, De Zaale 20, 5612 AJ Eindhoven, The Netherlands.
The Journal of Physical Chemistry Letters
|April 23, 2021
Summary
Developing new materials for low-temperature solid oxide fuel cells (SOFCs) is crucial. This study identifies 31 promising perovskite candidates for SOFC electrodes and electrolytes using computational screening.
Area of Science:
- Materials Science
- Electrochemistry
- Computational Chemistry
Background:
- Designing low-temperature solid oxide fuel cell (SOFC) materials for oxygen permeability is a significant challenge.
- Current SOFC materials often require high operating temperatures, limiting their widespread application.
Purpose of the Study:
- To computationally screen ABO3 and A0.5AI0.5BO3 cubic perovskites for potential use as SOFC electrode and electrolyte materials.
- To identify perovskite compositions with favorable oxygen vacancy formation energy (E_vac) and area-specific resistance (ASR).
Main Methods:
- High-throughput density functional theory (DFT) calculations were employed to determine E_vac for numerous perovskite compositions.
- Area-specific resistance (ASR) was calculated based on E_vac to assess oxygen reduction reaction activity and ionic conductivity.
- A predictive model was developed to estimate E_vac and ASR for complex perovskites using data from simpler ones.
Main Results:
- A dataset of E_vac was generated for a pool of all-inorganic ABO3 and A0.5AI0.5BO3 cubic perovskites.
- 31 perovskite compositions were identified as promising candidates, exhibiting properties suitable for SOFC cathodes and oxygen permeation.
- An intuitive method was established to predict the properties of complex perovskites from simpler analogues.
Conclusions:
- The study successfully identified novel perovskite materials for low-temperature SOFC applications.
- The developed computational screening approach and predictive model can accelerate the discovery of advanced materials for energy technologies.
- This work provides a pathway for exploring a wider range of perovskite materials for diverse energy applications.
