Predicting oxygen vacancy formation energy in metal oxides

The oxygen vacancy formation energy is a key quantity determining materials chemical and thermodynamic properties, and plays a fundamental role in a variety of energy applications. Large scale prediction of this quantity is therefore a particularly attractive endeavour, and high-throughput DFT and machine learning can be powerful tools to this end.

By performing high throughput DFT studies within the Open Quantum Materials Database (OQMD), we produced a dataset comprising thousands of oxygen vacancy formation energy calculations of materials with a broad range of compositions and structures. Here, we first present the results of such studies, and then utilize the dataset to train multiple machine learning models. We compare the performance of the different models, and analyze the influence of various descriptors, identifying the ones playing the largest role in maximizing the predictive accuracy.