基于原子的机器学习模型,用于色素和替代色素电子性质的定量性质结构关系
Tuan H Nguyen1, Khang M Le2, Lam H Nguyen2,3
1Faculty of Chemical Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City 7000000, Vietnam.
ACS omega
|October 23, 2023
概括
机器学习模型可以准确地预测电子属性,比如电子亲和力和化的电离潜力. 这种定量结构-属性关系 (QSPR) 方法利用图核和高斯过程回归用于各种化学类.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 机器学习 机器学习
背景情况:
- 预测有机分子的电子性质对于材料设计至关重要.
- 定量结构与属性关系 (QSPR) 模型提供了一个数据驱动的方法.
- 烯及其衍生物是重要的有机半导体.
研究的目的:
- 开发精确的基于机器学习的QSPR模型,用于预测电子亲和力,电离潜力和带隙.
- 为了评估基于原子的Weisfeiler-Lehman (WL) 图核与高斯过程回归 (GPR) 结合的性能.
- 评估积极学习对各种化学数据集的有用性.
主要方法:
- 采用了基于原子的Weisfeiler-Lehman (WL) 图核方法的三种变体.
- 使用高斯过程回归器 (GPR) 机器学习模型.
- 在B3LYP-D3/6-31+G(d) 层面使用密度函数理论 (DFT) 计算的分子性质.
主要成果:
- 实现了对多环芳 (PAH) 和其衍生物的电子性质的准确预测.
- 对于预测的电子性质,证明了较低的平方根平均偏差 (0.15 eV).
- 展示了积极学习对多样化和复杂数据集的有效性.
结论:
- GPR/WL内核方法提供了一个强大的框架,用于预测fusen的电子特性.
- 模型可解释性提供了对控制电子性质的潜在化学因素的洞察.
- 开发的QSPR模型是加速发现新有机电子材料的宝贵工具.
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