| Abstract Scope |
LaCoO3-based perovskite shows promise as solid oxide cell (SOC) air electrode materials. To enhance its performance, researchers typically incorporate dopants to improve stability and oxygen ionic conductivity, properties that can be calculated via first-principles simulations. However, the vast number of potential dopants, concentrations, and combinations renders such calculations computationally intractable using conventional potentials from VASP. This study presents a novel high-throughput approach leveraging neural network potential (NNP) developed by Matlantis to optimize dopant configurations in LaCoO3. We systematically investigate the effects of 20 distinct dopant elements, encompassing both A-site (Mg, Ca, Sr, Ba, Ce, Pr, Nd, Sm, Gd) and B-site (Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Al, Ga) substitutions, on the material's stability and oxygen ionic conductivity. Our methodology demonstrates a significant advancement in the computational exploration of doped perovskites, offering a more efficient alternative to conventional calculations. The findings provide valuable insights for future SOC development. |