Graph neural network modeling of vacancy formation enthalpy for materials discovery and its application in solar thermochemical water splitting

We present a graph neural network modeling approach that fully automates the prediction of the DFT-relaxed vacancy formation enthalpy of any crystallographic site from its DFT-relaxed host structure. Applicable to arbitrary structures with an accuracy limited principally by the amount/diversity of the data on which it is trained, this model accelerates the screening of vacancy defects by many orders of magnitude by replacing the (up to 100s of) DFT supercell relaxations required for each symmetrically unique crystal site. 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 of DFT-relaxed crystal structures. We demonstrate the model's practical utility by high-throughput screening metal oxides from the Materials Project to identify high potential candidates for solar thermochemical water splitting. Ultimately, this modeling approach provides a significant screening and discovery capability for any application in which vacancy defects are the primary driver of a material's utility.