Prediction of emission energy of Cr3+ in oxides based on first-principles calculations and machine learning

Abstract

Abstract The first-principles calculations of the energies of the emission level (2Eg) of Cr3+ in 10 oxide crystals were performed using the first-principles configuration-interaction calculations. In order to improve the accuracy of the prediction, a machine learning model was created by using the results of the first-principles calculations and the experimental 2Eg energies as the training data. The predicted values using this model showed good correlation with the experimental values, where the correlation coefficient is 0.92. The obtained predicted model indicated that the Cr 3d component of t2g molecular orbital is the most important quantity for prediction of the 2Eg energy.