对光电子分子的垂直激发和发射能量的预测:结合高通量计算和机器学习的自动化工作流程
Lifeng Zheng1, Zhongye Wang1, Jiawei Chen1
1State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Engineering Research Center of Photoresist Materials of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
The journal of physical chemistry letters
|June 16, 2025
概括
研究人员开发了一种机器学习模型,以预测光电子材料的性能. 这种方法加速了新型有机分子的选,以提高光电子设备中的电荷传输.
科学领域:
- 材料科学 材料科学 材料科学
- 有机电子 有机电子
- 计算化学的计算化学
背景情况:
- 有机光电子材料由于其激发性质,对设备至关重要.
- 预测激发状态属性对于材料设计至关重要,但通常是计算密集的.
研究的目的:
- 开发一个数据驱动的工作流程,以便快速预测激发状态属性.
- 使用机器学习对光电子应用的候选分子进行选.
主要方法:
- 编制了1223个有机分子的数据库,包括捐赠/接受复合体.
- 机器学习模型使用基态Dr指数进行训练,以预测激发状态属性.
- 博士描述符,激发和辐射能量之间的关系被确立.
主要成果:
- 基态Dr指数有效预测电荷转移趋势和兴奋状态Dr*指数.
- 机器学习模型重现了实验结果,没有复杂的兴奋状态计算.
- 具有3-6个化环和N/S异质原子的π合单位显示出强烈的电荷转移.
结论:
- 快速,数据驱动的工作流程用于选光电子材料已成功实施.
- 机器学习准确地预测激发状态属性,加速材料发现.
- 特定的π-结合结构对高性能光电子应用具有前景.
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