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使用主动机器学习和量子化学模拟,探索和设计基于碳点的长后发光材料

Hongwei Yang¹, Zhun Ran¹, Yimeng Luo¹

¹Key Laboratory for Biomass Materials and Energy of Ministry of Education/Guangdong Provincial Engineering Technology Research Center for Optical Agriculture, College of Materials and Energy, South China Agricultural University, Guangzhou 510642, China.

|October 8, 2024

概括

机器学习,特别是XGBoost,可以准确地预测碳点 (CD) 属性. 这种方法导致合成了具有报告寿命最长的新型长光后光材料,加速了先进材料的开发.

科学领域:

光学和光子学在光学和光子学.
材料科学材料科学材料科学
计算化学计算化学

背景情况:

碳点 (CD) 由于低成本和无毒性,对光学来说是有希望的.
合成具有特定属性的CD是具有挑战性的,因为复杂的,未知的结构.
有针对性的合成需要理解结构-属性关系.

研究的目的:

应用机器学习来探索和优化碳点的光后特性.
开发一种针对新型长光后光材料的有针对性的合成方法.
为了阐明碳点前体中的结构功能关系.

主要方法:

利用机器学习,特别是XGBoost算法,用于非线性拟合和属性预测.
采用贝叶斯优化来选和合成基于碳点的材料.
结合量子化学计算与实验数据进行结构功能分析.

主要成果:

XGBoost实现了高预测准确度,以获得最佳的激发/发射波长和光后寿命.
通过一步微波方法,成功合成了基于碳点的长光后发光材料,其报告寿命最长.
使用电子孔分析揭示了碳点及其前体之间的内在结构功能关系.

结论:

机器学习有效地建立了前体和具有未知结构的材料之间的非线性相关性.
开发的方法简化了先进光学材料的材料设计过程.
这项研究加速了基于碳点的新型长光后发光材料的发现和开发.

关键词:

之后的光之后的光碳点是指碳点的部分.图形化用户界面图形化用户界面机器学习是机器学习.量子化学是一种量子化学.

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