在光子材料中的稀土离子的原子模型
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.
这篇评论结合了量子化学和机器学习,用于光子应用的稀土离子模型. 它为开发激光,传感器和量子记忆的新材料提供了指南.
科学领域:
- 材料科学
- 量子化学
- 计算物理
背景情况:
- 由于其独特的光学特性,稀土离子对光子技术至关重要.
- 由于局部化的4f轨道和复杂的电子结构,模拟REI具有挑战性.
- 准确的理论建模对于设计基于REI的先进光子材料至关重要.
研究的目的:
- 审查和对稀土离子的原子模型策略进行比较.
- 探索量子化学和机器学习 (ML) 之间的协同作用.
- 为设计下一代基于REI的光子材料提供指南.
主要方法:
- 测试传统的量子化学方法,如DFT+U,HSE06,GW和DMFT.
- 使用波函数方法,如CASSCF和CASPT2,进行详细的电子结构分析.
- 开发和应用在DFT数据上训练的ML模型来预测材料特性和反向设计.
主要成果:
- 混合DFT方法可以准确地复制Ce3+:YAG中的4f-5d间隙.
- 波函数方法捕捉了Eu3+:Y2SiO5中的斯特克分裂和过渡强度.
- 机器学习模型能够高准确度地预测带隙,并使光体的反向设计具有更好的热稳定性.
结论:
- 将量子化学与机器学习结合起来,
- 这种综合策略有助于合理设计各种光子应用的材料.
- 对于稀土光子领域的研究人员来说,这篇评论是一个宝贵的资源.
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