机器学习辅助的混合量子-经典动力学,没有明确的非adiabatic合:应用到氧酸的光解离
Mahesh K Sit1, Subhasish Das1, Kousik Samanta1
1School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Argul, Odisha 752050, India.
The journal of physical chemistry. A
|September 16, 2024
概括
这项研究引入了一种混合量子-经典方法,使用机器学习的潜在能量表面来建模分子光解离. 过氧酸中的N-O键很可能会断裂,形成HO2和NO2碎片.
科学领域:
- 量子化学是一种量子化学.
- 计算化学是一种计算化学.
- 化学动力学 化学动力学
背景情况:
- 在分子光解离动力学中,非adiabatic效应至关重要.
- 计算非adiabatic合元件可能会在计算上昂贵.
- 机器学习为模拟复杂的潜在能量表面提供了一个潜在的解决方案.
研究的目的:
- 开发和应用一种新的混合量子-经典方案来模拟分子光解离.
- 通过机器学习的潜在能量表面 (PES) 绕过非adiabatic合元件的明确计算.
- 为了研究过氧酸 (HO2NO2) 的光解离路径.
主要方法:
- 开发了一种混合量子-经典方法,将机器学习的PES与最少开关的表面跳转方法集成在一起.
- 用CASSCF计算了HO2NO2六个最低电子状态的adiabatic PES数据.
- 机器学习模型在计算的 PES 数据上受过训练,以预测 PES 值.
- 在S4状态上进行了动力学模拟,结合了非adiabatic过渡.
主要成果:
- 开发的方案成功模拟了过氧酸的光解离.
- 模拟显示了NO键解离的高概率.
- 确定的主要解离产物是HO2和NO2基.
结论:
- 混合量子-经典方案与机器学习PES是一种有效的方法来研究非adiabatic动态.
- 过氧酸中的N-O键易受光解离.
- 这种方法为分子的光化学行为提供了宝贵的见解.
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