来自QCT和机器学习的星际云与星际云相关的C和O2反应碰撞的速率系数
Xia Huang1, Xin-Lu Cheng1,2, Hong Zhang2
1Institute of Atomic and Molecular Physics, Sichuan University, Chengdu, China.
The Journal of chemical physics
|November 8, 2024
概括
本研究引入了一种机器学习方法,用于计算碳 (C) 和氧 (O2) 等星际分子的反应速率. 这种方法有效地提供了对天体化学至关重要的精确波动速率系数.
科学领域:
- 天体化学和计算化学.
- 量子力学和分子动力学 量子力学和分子动力学
背景情况:
- 星际分子经历了外热和无障碍反应,使得在低天文温度下能够快速反应.
- 对于像C+O2这样的反应,精确的状态选择速率系数对于理解星际和大气环境至关重要.
- 确定这些参数的传统计算方法通常是计算密集的.
研究的目的:
- 开发和验证一种计算方法,将准经典轨迹 (QCT) 计算与机器学习 (ML) 结合起来,以确定国家选择的速率系数.
- 显著降低对C + O2系统反应截面和速率系数计算的计算要求.
- 为天体物理建模提供C-O2碰撞系统的全方位振动速率系数数据集.
主要方法:
- 使用准经典轨迹 (QCT) 计算.
- 实施机器学习技术,特别是神经网络 (NN) 和高斯过程回归 (GPR),以建模反应动态.
- 在50-1500 K的温度范围内,根据明确的数值计算验证了ML模型.
主要成果:
- 实现了显著的计算成本降低,同时保持了状态选择反应截面和速率系数计算的准确性.
- 基于NN和基于GPR的模型都显示出高精度,在研究的温度范围内确定系数 (R2) >0.96.
- 产生了迄今为止最广泛的数据集,用于C-O2系统的反振速率系数 (v = 0-4,j = 0-70 → v' = 0-15).
结论:
- 结合QCT和ML方法是一种计算效率高,准确的方法,用于确定星际反应的状态选择速率系数.
- 由此产生的综合数据集 - - 波动速率系数 - - 将极大地帮助C-O2碰撞系统的天体物理建模.
- 这项工作为将类似的ML加速方法应用于其他复杂的天体化学相关反应铺平了道路.
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