Prediction of 4f-5d Transition Energy of Ce3+ in Oxides By First-Principles Calculations and Machine Learning

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A Ce3+ ion has only one electron in the 4f orbitals and its 4f-5d transition energy is strongly influenced by the width of the crystal field splitting of the 5d orbitals. In other words, the absorption and emission wavelengths of Ce3+ in crystals can be widely controlled by changing the ligand field environment. As an example of Ce3+-doped phosphor, Ce:YAG (Y3Al5O12) is widely used as an yellow phosphor for standard white LEDs. Therefore, novel phosphors with desired emission colors can be developed by adding Ce3+ to appropriate host crystals.In this work, we aimed to construct an efficient prediction model for the transition energy of Ce3+ in oxide crystals. Small clusters consisting of the substituted Ce3+ ion and the first-neighbor anions were constructed and molecular orbital calculations were performed using the relativistic discrete-variational Xα (DV-Xα) method. Then machine learning based on structural and electronic state parameters was performed to create a predictive model for the 4f-5d transition energy of Ce3+ in crystals. By creating the correlation diagrams, the influence of each parameter on the transition energy was analyzed in detail.