光子材料における稀土イオンの原子モデル化
Dennis Delali Kwesi Wayo1, Mohd Zulkifli Bin Mohamad Noor1, Masoud Darvish Ganji2
1Faculty of Chemical and Process Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, Pahang, Malaysia.
Luminescence : the journal of biological and chemical luminescence
|September 5, 2025
まとめ
このレビューでは 量子化学と機械学習を組み合わせて フォトニックアプリケーション用の 稀土イオンをモデル化しています レーザー,センサー,量子記憶のための 新しい材料の開発のガイドです
科学分野:
- 材料科学
- 量子化学について
- 計算物理
背景:
- 希土イオン (REI) は 光学技術において 独特の光学特性があるため 極めて重要です
- REIのモデリングは,局所化された4f軌道と複雑な電子構造のために困難です.
- REIベースの高度な光子材料の設計には,正確な理論モデリングが不可欠です.
研究 の 目的:
- 稀土イオンの原子モデル化戦略をレビューし,比較する.
- REI研究における量子化学と機械学習 (ML) の相乗効果を探求する.
- REIベースの次世代光子材料の設計のためのガイドを提供すること.
主な方法:
- DFT+U,HSE06,GW,DMFTのような伝統的な量子化学の方法のベンチマーク
- 詳細な電子構造分析のためにCASSCFとCASPT2などの波動関数方法を使用します.
- 材料の性質と逆設計を予測するために,DFTデータで訓練されたMLモデルを開発し,適用する.
主要な成果:
- ハイブリッドDFT方法は,Ce3+:YAGの4f-5dギャップを正確に再現します.
- 波動関数法では,Eu3+:Y2SiO5におけるスターク分裂と移行強度を捉えます.
- MLモデルは高精度でバンドギャップを予測し,熱的安定性を向上させた逆設計を可能にします.
結論:
- 量子化学と機械学習の組み合わせは REIをモデル化するための強力なアプローチを提供します
- この統合された戦略は,多様な光学アプリケーションのための材料の合理的な設計を容易にする.
- このレビューは,希土光学分野の研究者にとって貴重な情報源となる.
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