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反应速率系数和机器学习预测O(3P) +C2(X1Σg+) 在精确的PIP-NN潜在能量表面上的碰撞

Xia Huang¹, Guosen Wang¹, Changmin Guo¹

¹Institute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, China.

The Journal of chemical physics

|February 13, 2025

概括

本研究详细介绍了[CCO]系统的新潜在能量表面,这对于理解O + C2反应至关重要. 计算表明反应道占主导地位,神经网络模型有效地预测了反应截面.

科学领域:

理论化学理论化学
化学动力学化学动力学
计算化学计算化学

背景情况:

了解氧原子与碳分子的反应动态对于燃烧和天体化学至关重要.
精确的潜在能量表面 (PES) 对于模拟化学反应机制至关重要.
之前的研究可能缺乏O + C2系统的高维PES或详细的机制洞察力.

研究的目的:

为[CCO]系统的3A′′状态构建一个全维,准确的潜在能量表面 (PES).
为了研究O(3P) +C2(X1Σg+) →CO(X1Σ+) +C(3P) 碰撞的微观化学反应机制.
开发一个计算效率高的模型来预测反应截面.

主要方法:

通过使用神经网络 (NN) 构建了一个全局分析 PES,该神经网络具有 permutationally 不变的多项式,适用于 9293 ab initio 能量.
准经典轨迹 (QCT) 方法被用来研究反应动态.
开发了一种混合方法,将QCT与NNs结合起来,以预测横截面.

主要成果:

构建的 PES 准确地代表了 [CCO] 系统.
量子试验计算得出反应截面和速率系数,与文献数据相一致.
反应道的速率系数显著超过100020,000K的不弹性道的速率系数.
基于NN的模型准确地复制了QCT结果,从而降低了计算成本.

结论:

该研究提供了可靠的PES,并详细了解了O + C2反应机制.
在O + C2碰撞中,反应道在广泛的温度范围内占主导地位.
开发的基于NN的模型证明了加速化学动力学模拟的有希望的策略.

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