将密度函数理论与机器学习相结合,用于增强金属氧化物中带隙预测
Chidozie Ezeakunne1, Bipin Lamichhane2, Shyam Kattel1
1Department of Physics, University of Central Florida, Orlando, FL 32816, USA. shyam.kattel@ucf.edu.
Physical chemistry chemical physics : PCCP
|February 25, 2025
概括
密度函数理论与哈伯德U校正 (DFT+U) 和机器学习 (ML) 准确预测金属氧化物特性. 包括氧和金属轨道的U值提高了准确性,ML提供了具有成本效益的预测.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 固态物理 固态物理
背景情况:
- 准确预测材料特性,如带间隙和格子参数,对于发现新的功能材料至关重要.
- 密度函数理论与哈伯德U校正 (DFT+U) 是一种常见的方法,但在计算上可能很昂贵.
- 机器学习 (ML) 为加速材料属性预测提供了一个潜在的替代方案.
研究的目的:
- 准确预测各种金属氧化物的带间隙和格子参数,使用混合DFT+U和ML方法.
- 为了研究哈巴德U校正对氧2p轨道以及金属3d/4f轨道的影响.
- 开发一个具有成本效益的ML模型,可以复制金属氧化物的DFT+U结果.
主要方法:
- 对TiO2 (rutile和anatase),立方ZnO,ZnO2,CeO2和ZrO2进行了广泛的DFT+U计算.
- 确定了用于DFT+U计算的最佳 (Up, Ud/f) 整数对,以匹配实验数据.
- 开发和训练监督的ML模型,使用DFT+U数据来预测带间隙和格子参数.
主要成果:
- 为每个金属氧化物确定了最佳 (Up, Ud/f) 值,大大提高了预测准确度.
- 例如,最优对包括 (8 eV,8 eV) 的鲁TiO2和 (6 eV,12 eV) 的c-ZnO.
- 监督的ML模型以计算成本的一小部分准确地复制了DFT+U结果,并显示出良好的概括性.
结论:
- 结合的DFT+U和ML方法提供了对金属氧化物性能的准确和高效预测.
- 将氧轨道与金属轨道一起进行U校正是提高DFT+U精度的关键.
- 开发的ML模型作为一个快速的DFT前估计工具,补充现有的高通量框架.
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