A crystal graph neural network model for defect formation in clean energy materials
Broad materials screening for defect properties requires fast methods that circumvent the need for first-principles supercell calculations of a vast number of possible defect configurations. We construct and train a defect graph neural network to screen oxides for clean energy applications.
