国家到国家整体截面的高斯过程回归:O + O2碰撞分离反应的案例
Jiawei Yang1, Jia Li1, Junhong Li1
1School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing 401331, China.
The journal of physical chemistry. A
|June 13, 2024
概括
高斯过程回归 (GPR) 使用有限的数据准确地模拟高超音速反应动态. 这种方法加快了复杂的模拟,使得高超音速流中的高温反应能够准确预测.
科学领域:
- 计算化学和物理计算化学和物理
- 航空航天工程 航空航天工程
- 化学动力学 化学动力学
背景情况:
- 超音速车辆研究至关重要,但在超音速流中模拟高温,不平衡反应在计算上具有挑战性.
- 原子二原子反应中大量可访问的状态使得对状态对状态反应概率的详尽分析变得困难.
研究的目的:
- 为O + O2 → 3O离散反应开发一个准确和高效的模型,用于预测O + O2 → 3O离散反应的状态对状态积分截面 (ICS).
- 为了最大限度地减少数据采样要求,以适应复杂的反应动态模型.
主要方法:
- 使用高斯过程回归 (GPR) 在有限的数据集 (807个量子经典轨迹 (QCT) 数据点) 上进行训练.
- 采用信心区间和Kullback-Leibler (KL) 差异,以优化GPR模型收的数据采样.
- 应用训练的GPR模型来预测随机初始条件 (转换能量,振动和旋转状态) 的状态到状态的ICS.
主要成果:
- 汇聚的GPR模型是在大约一个月内开发的,可以在几秒钟内预测ICS值.
- 对于330个未见的初始条件,GPR预测显示出高准确度,平均平方误差 (MSE) 为0.08 Å2,R2为0.9986.
- 使用GPR预测的州对州ICS计算的平衡分离率系数与现有的实验和理论数据一致.
结论:
- 高斯过程回归为计算上昂贵的直接状态对状态反应动态计算提供了有效和准确的替代方案.
- 开发的GPR模型显著加速了与超音速流相关的不平衡高温反应的模拟.
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