Michael L. Klein

Transition metal oxide materials are of great utility, with a diversity of topical applications ranging from catalysis to electronic devices. Because of their widespread importance in materials science, there is increasing interest in developing computational tools capable of reliable prediction of transition metal oxide phase behavior and properties. The workhorse of materials theory is density functional theory (DFT). Accordingly, we have investigated the impact of various correlation and exchange approximations on their ability to predict the properties of NiO using DFT. We have chosen NiO as a particularly challenging representative of transition metal oxides in general. In so doing, we have provided validation for the use of the r2SCAN density functional for predicting the materials properties of oxides. r2SCAN yields accurate structural properties of NiO and a local spin moment that notably persists under pressure, consistent with experiment. The outcome of our study is a pragmatic scheme for providing electronic structure data to enable the parameterization of interatomic potentials using state-of-the-art artificial intelligence (AI) and machine learning (ML) methodologies. The latter is essential to allow large scale molecular dynamics simulations of bulk and surface materials phase behavior and properties with ab initio accuracy.