数据驱动的机器学习模型用于预测单化的最大吸收和排放波长
Yongshi Jin1, Zhaohe Wang1, Miao Dong2
1School of Cyberspace Security, Hainan University, Haikou 570228, China; School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|September 27, 2024
概括
本研究引入了一种机器学习模型,用于预测单化 (SBF) 特性. 该SBFs-ML模型准确地估计了吸收和排放波长,有助于设计新的SBFs.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 单化物 (SBF) 在生物成像中非常有价值,因为它们的制备简单,分子量低,扩散良好.
- 精确预测光物理性质,如最大吸收和辐射波长,对于开发先进的SBF至关重要.
研究的目的:
- 开发一种高度准确的机器学习模型,用于预测SBF的最大吸收和排放波长.
- 通过创建一个可解释的模型来应对有限数据的挑战.
主要方法:
- 利用一个全连接的神经网络与计算化学技术相结合.
- 在81个SBF的数据集上训练模型,专注于可解释性的三个关键描述符.
主要成果:
- 开发的模型,SBFs-ML,实现了高精度,平均相对误差为1.54%的吸收和2.93%的发射波长.
- 该模型的预测通过实验结果得到验证,证实了它们的可靠性.
- 仅使用三种描述符可以确保模型预测的强烈可解释性.
结论:
- SBFs-ML模型提供了一种可靠和可解释的方法来预测SBF的光物理性质.
- 这种方法显著促进了新型SBFs用于各种应用的高效设计和合成.
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