TY - JOUR
AU - Hiroyuki Hori
AU - Shota Takemura
AU - Hayato Obata
AU - Kazuyoshi Ogasawara
T1 - Prediction of 4f7-4f65d1 Transition Energy of Eu2+ in Oxides Based on First-principles Calculations and Machine Learning
T2 - Advance Sustainable Science, Engineering and Technology
JF - Advance Sustainable Science, Engineering and Technology
DA - 2020///
PY - 2020
IS - 1
VL - 2
AB - In order to establish a method to predict the 4f7-4f65d1 transition energy of Eu2+ in oxides, linear regression models were created based on first-principles calculations and machine learning. The model clusters consisting of the central Eu2+ and O2- ions closer than the nearest cation were constructed and the 4f7-4f65d1 absorption energy of Eu2+ in these clusters were calculated by first-principles many-electron calculation using the relativistic discrete variational multi-electron (DVME) method. However, the 4f7-4f65d1 absorption energies of Eu2+ in oxides calculated by relatively simple first-principles calculations tend to be overestimated by ca. 1.6 eV. In order to improve the accuracy of the prediction, we performed machine learning considering the calculated absorption energy as well as the other electronic and structural parameters as the attributes. As a result, the regression formula to predict the 4f7-4f65d1 absorption energy of Eu2+ in oxides has been created by machine learning. The 4f7-4f65d1 absorption energy predicted by this model are in good agreement with the experimental ones. Therefore, accuracy of the prediction was significantly improved compared to the simple first-principles calculations. In a similar way, a predictive model of the 4f65d1-4f7 emission energy of Eu2+ in oxides has been also created
DO - 10.26877/asset.v2i1.6212
ER -