使用DFT/MRCI优化最小能量圆交叉点的优化(2)
Tzu Yu Wang1, Simon P Neville2, Michael S Schuurman1,2
1Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa K1N 6N5,Canada.
Journal of chemical theory and computation
|January 29, 2025
概括
本研究引入了一种新的高斯过程回归方法,用密度函数理论和多引用配置相互作用 (DFT/MRCI) 来计算平滑的潜在能量表面. 这种方法通过学习光滑的表面,甚至在形交叉点上,提高了电子光谱学的模拟.
科学领域:
- 计算化学是一种计算化学.
- 理论化学是一种理论化学.
- 量子化学是一种量子化学.
背景情况:
- 密度函数理论和多引用配置交互 (DFT/MRCI) 方法的结合为电子激发状态提供了计算效率和准确性.
- 一个关键的挑战是构建光滑的潜在能量表面,因为选择的CI程序的不连续性.
研究的目的:
- 开发一种方法,从DFT/MRCI计算中学习光滑的潜在能量表面.
- 解决潜在能量表面的不连续性,特别是在形交叉点.
主要方法:
- 利用高斯过程回归来将不连续性视为噪声.
- 在回归框架中整合和优化了一个白噪声内核.
- 学习了特征多项式系数表面,而不是附加的能量.
主要成果:
- 成功学习了乙烯,丁和富尔文等分子的光滑潜在能量表面.
- 使用已学习的表面优化了最小能量的形交点几何形状.
- 获得的结构和分支空间与初始MRCI结果相比.
结论:
- 高斯过程回归方法为学习光滑的DFT/MRCI表面提供了一种可行的方法.
- 这种技术通过处理表面不连续性来改善电子光谱仪的模拟.
- 该方法与高层次的初始计算有很好的一致性.
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