神经网络潜能促进分子共振的精确复杂缩放:从模型到高维现实系统
Zhen Xu1,2, Siting Hou1,2, Zhimo Wang1,2
1Institute of Modern Physics, Northwest University, Xi'an 710127, China. chjxie@nwu.edu.cn.
Physical chemistry chemical physics : PCCP
|August 6, 2024
概括
一种新的基于神经网络的复杂缩放 (NN-CS) 方法准确计算分子共振固有值. 这种方法对复杂的系统具有前景,包括高维光解离动力学.
科学领域:
- 量子化学 是一个量子化学.
- 计算化学计算化学
- 理论化学 理论化学
背景情况:
- 分子共振对于理解化学动态至关重要,特别是在光解离过程中.
- 精确计算共振特性,如能量和寿命,是必不可少的,但由于复杂的潜在能量表面和高维度而具有挑战性.
研究的目的:
- 引入和验证一种基于神经网络的新型复杂缩放 (NN-CS) 方法,用于计算分子共振的复杂固有值.
- 证明NN-CS方法在各种系统中的有效性,从简化模型到复杂的高维光解离动态.
主要方法:
- 开发一种基于神经网络的复杂缩放 (NN-CS) 方法,以有效地实现非赫米特汉密尔顿数内的潜在能量表面的复杂缩放.
- 将NN-CS方法应用于具有形交叉点的2D糖尿病模型,以验证自身值计算.
- 使用NN-CS方法与2D哈密尔顿和一个新的神经网络糖尿病潜在能量矩阵来研究安 photodissociation.
- 将NN-CS方法扩展到对氨 (NH3) 具有挑战性的6D光解离连续体.
主要成果:
- 在2D模型中,NN-CS方法准确地复制了共振状态的固有值.
- 在 thioanisole 光解离中对振动共振的计算寿命与现有的理论和实验数据有很好的一致性.
- 该NN-CS方法成功计算了NH3光解离中的6D共振的能量位置和宽度,与其他理论结果保持一致.
- 该方法在处理多个合电子状态和高维系统方面表现出了强度.
结论:
- 拟议的NN-CS方法为计算分子共振特性提供了准确和高效的方法.
- 这种方法能够在具有多个合电子状态的系统中准确处理振动共振.
- 该NN-CS方法显示了应用于复杂,高维现实的化学系统的巨大潜力,进步了计算化学领域.
相关概念视频
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