机器学习使用电子和结构描述器预测内部转换的速率常数
Rashid R Valiev1, Rinat T Nasibullin1, Dage Sundholm1
1Department of Chemistry, Faculty of Science, University of Helsinki, P.O. Box 55 (A.I. Virtanens plats 1), Helsinki FIN-00014, Finland.
The journal of physical chemistry letters
|October 7, 2025
概括
机器学习模型使用分子描述器准确预测内部转换率常数 (k_IC). 这种方法绕过了计算密集的量子力学计算来确定光物理性质.
科学领域:
- 计算化学是一种计算化学.
- 光物理学的光学物理学
- 机器学习 机器学习
背景情况:
- 内部转换 (IC) 是一个关键的光物理过程,使激发电子状态失活.
- 准确预测IC速率常数 (k_IC) 对于理解分子行为至关重要,但在计算上具有挑战性.
研究的目的:
- 开发一种机器学习 (ML) 方法来预测第一个激发单元状态 (S1) 和基本状态 (S0) 之间的k_IC.
- 使用分子电子和结构描述器进行精确的k_IC估计.
- 为传统的量子力学计算提供一个成本效益高的替代方案.
主要方法:
- 通过使用成本效益高的能量传输模型生成了超过5000个k_IC值的数据集.
- 在参数计算中使用时间依赖密度函数理论 (TDDFT).
- 训练有素的CatBoost (CB) 和神经网络 (NN) 模型,包括变压器和基于图形的架构.
主要成果:
- 在CB和NN模型中实现了高预测准确性,R2值为0.99左右.
- 通过使用能量增强数据集,成功预测了新型分子的k_IC.
- 引入了基于图形的新型X-H键描述器,用于预测各种化学类的速率常数.
结论:
- 开发的ML模型为预测k_IC提供了一个高度准确和高效的方法.
- 分子电子和结构描述器,特别是基于图的X-H键描述器,是光物理性质的有效预测器.
- 这种方法显著降低了与确定光物理性质相关的计算成本.
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