Materials Discovery for High-Temperature, Clean-Energy Applications Using Graph Neural Network Models of Vacancy Defects and Free-Energy Calculations

High-temperature properties of oxides are often governed by O vacancy defect formation. To enable high-throughput screening of thermochemical materials for clean-energy applications, we developed a graph neural network modeling approach for defects. This model accelerates the screening of vacancy defects by many orders of magnitude by replacing the supercell relaxations in density functional theory (DFT) that are otherwise required for each symmetrically unique crystal site. It fully automates the prediction of the DFT-relaxed vacancy formation enthalpy of any crystallographic site based only on the primitive cell of the host crystal structure. It can thus be used off-the-shelf to rapidly screen 10,000s of crystal structures (which can contain millions of unique defects) from existing databases. This modeling approach therefore provides a significant screening and discovery capability for a plethora of applications in which vacancy defects are the primary driver of a material’s utility. For example, by high-throughput screening the Materials Project’s metal oxides, we rapidly “re-discover" and identify new high potential candidate materials for hydrogen generation via solar thermochemical water splitting and energy storage, for CO2 conversion via reverse water gas shift chemical looping, and for cathodes in solid oxide fuel cells. Thermodynamic modeling on the basis of the high-throughput screening results allows us to connect the predicted defect energies to high temperature process conditions relevant to the different application areas, and we extract the reduction entropies as an additional selection criterion for high-performance materials.