机器学习驱动的预测超快旋转放松在金属化物矿中,用于旋转应用
Jianhui Li1, MingXi Chen2, Pan Wang3
1State Key Laboratory of Quantum Functional Materials, SUSTech Energy Institute for Carbon Neutrality, Department of Mechanical and Energy Engineering, Southern University of Science and Technology Shenzhen Guangdong 518055 China chenxh@sustech.edu.cn.
Chemical science
|October 24, 2025
概括
研究人员开发了一种预测模型,用于矿中旋转放松的旋转电子学. 该模型使用固有的材料特性来指导节能电子设备的设计.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 量子化学 是一个量子化学.
背景情况:
- 斯宾特罗尼克斯为电子设备提供了降低的能源消耗.
- 矿正在成为自旋电子应用的关键材料.
- 在矿中控制旋转放松至关重要,但对于设备设计来说具有挑战性.
研究的目的:
- 开发一个超快旋转放松率在矿的预测模型.
- 为了确定控制旋转放松的内在材料描述器.
- 建立新型自旋电子矿的预合成设计策略.
主要方法:
- 人工神经网络 (ANN) 模型构建.
- 对52种矿材料的数据集进行培训.
- 对14种合成化合物的超快自旋动力学测量.
- 为了模型的可解释性,夏普利添加式解释 (SHAP).
主要成果:
- 高预测准确度 (R2 = 0.99) 通过交叉验证实现.
- 确定了关键描述符:HOMO/LUMO能量,分子量,极化性,二极子时刻.
- 澄清了机制:旋转轨道合,声子散射,电子孔交换.
结论:
- 为旋转放松建立了一个可概括和可解释的预测模型.
- 演示了旋转功能矿的合理设计策略.
- 促进了节能自旋电子设备的开发.
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