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使用机器学习方法探索合体CdSe纳米板块的温度依赖的光发光学动力学

Ivan P Malashin¹, Daniil Daibagya^2,3, Vadim Tynchenko²

¹Bauman Moscow State Technical University, Moscow, Russia, 105005. ivan.p.malashin@gmail.com.

Scientific reports

|December 27, 2024

概括

机器学习模型准确地预测了合体化纳米板块中的温度依赖的光发光谱. 这些模型预测了从0K到300K的光谱,有助于材料科学研究.

科学领域:

材料科学材料科学材料科学
量子点就是量子点.
频谱学是一种光谱学.

背景情况:

了解温度依赖的光发光 (PL) 对体半导体纳米板块 (NPL) 至关重要.
使用传统方法,在广泛的温度范围内预测PL光谱具有挑战性.

研究的目的:

探索机器学习 (ML) 技术,用于预测合体CdSe NPL中的温度依赖PL光谱.
预测PL光谱从85K到270K,向后转到0K,向前转到300K.
将ML预测与范和瓦尔什尼方程等理论模型进行比较.

主要方法:

使用多项式回归模型,包括Tweedie,LassoLars,线性回归,Ridge和Theil-Sen回归器.
在从85K到270K的实验PL数据上训练模型.
采用基于遗传算法 (GA) 的方法将实验数据与理论方程相匹配.

主要成果:

使用Tweedie回归的最佳6度多项式模型预测带能高达300K.
使用拉索拉斯和线性回归的9度模型适合向后预测到0K.
特定的Lasso,Ridge,Tweedie和Theil-Sen模型显示了在整个温度范围内对激电能预测的有效性.

结论:

机器学习有效地预测了CdSe NPL中的温度依赖的光发光谱.
开发的模型提供了准确的光谱预测,从0K到300K.
机器学习预测为材料表征的理论模型提供了有价值的补充.

关键词:

在CdSeSe中.发光的光度是非常的低.机器学习机器学习纳米板块是如何形成的时间动态的时间动态.

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