基于电子配对方法的自然轨道函数的占用数的软max参数化
Lizeth Franco1, Iván A Bonfil-Rivera1, Juan Felipe Huan Lew-Yee2
1Departamento de Física y Química Teórica, Facultad de Química, Universidad Nacional Autónoma de México, México City C.P. 04510, Mexico.
The Journal of chemical physics
|June 26, 2024
概括
我们开发了一种新的方法来参数化自然轨道函数理论中的职业数,使用深度学习组件. 这种方法提高了计算效率,并确保密度矩阵计算的N-表示性.
科学领域:
- 量子化学 是一个量子化学.
- 计算物理 计算物理
- 理论化学 理论化学
背景情况:
- 在自然轨道功能理论中,高效的计算依赖于对职位数和轨道的准确表示.
- 现有方法在计算性能和遵守N-可表示性原则方面可能面临挑战.
研究的目的:
- 为自然轨道功能理论引入职业数的创新参数化.
- 提高第一阶缩小密度矩阵 (1RDM) 计算的计算效率和N-表示性.
主要方法:
- 利用Piris自然轨道函数的电子配对方法.
- 采用软max函数,这是深度学习模型中的一个关键组件,用于参数化.
- 评估了使用W4-17-MR分子集的方法.
主要成果:
- 拟议的参数化确保了1RDM的N表示性.
- 在1RDM函数理论的计算效率方面取得了显著的改进.
- 与以前的实施相比,已经证明了更快,更强大的趋同.
结论:
- 使用 softmax 函数的新型参数化为自然轨道函数理论提供了一种计算效率高和N-可表示的方法.
- 这种方法代表了量子化学计算领域的重大进展.
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