Design of Doped Perovskite Oxygen Carriers Using Mathematical Optimization

Abstract Mathematical optimization can readily support the design of nanostructured materials in areas that are typically explored via design of experiments. The combinatorial nature of possible configurations of nanomaterials leads to a high-dimensional space, which can be efficiently explored by algorithms traditionally developed in process systems engineering. In this work, we demonstrate a mathematical optimization based framework for the design of doped perovskite oxygen carriers. Specifically, we developed an approach for incorporating evaluations from density functional theory into simplified structure-function relationships that can be embedded directly into mathematical models. The material design question was formulated as a mathematical optimization model and solved to identify patterns of dopant that are expected to yield a highly reducible material. The results from the dopant placement optimization model can inform the theoretical bounds of material performance as well as identify structures that can serve as targets for experimental synthesis.