使用神经网络方法构建的BeH2系统的全球糖尿病潜在能量表面
Chunhong Zhao1, Ruyi Liu1, Wentao Li1
1Weifang University of Science and Technology, Shouguang 262700, China.
The Journal of chemical physics
|December 17, 2025
概括
使用神经网络开发了BeH2的高质量的糖尿病潜在能量表面. 非adiabatic影响显著影响Be + H2反应,创建一个能量屏障和改变反应动态.
科学领域:
- 理论化学 理论化学
- 量子力学就是量子力学.
- 化学动力学 化学动力学
背景情况:
- 准确的潜在能量表面 (PES) 对于理解化学反应至关重要.
- 对话式 PES 对于描述电子状态之间的非对话式转换至关重要.
研究的目的:
- 为BeH2系统构建高质量的糖尿病PES.
- 调查非adiabatic效应在Be + H2反应中的作用.
主要方法:
- 具有对称性限制功能的神经网络方法.
- 在MRCI-F12/AVQZ层面进行高层次的初始计算.
- 动态计算使用亚得和糖尿病PES.
主要成果:
- 为BeH2电子状态开发了准确的糖尿病PES矩阵.
- 在Be+H2反应中确定了显著的非代作用.
- 观察到一个新兴的能量屏障和因非反性而改变的反应值.
结论:
- 新的糖尿病PES可以合理地描述电子状态转换.
- 非adiabatic影响在Be + H2反应动态中发挥着关键作用.
- 阿迪亚巴斯近似忽略了关键的非阿迪亚巴斯现象.
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