一种新的先验方法,以避免在CI计算中计算可忽略的哈密尔顿矩阵元素
Paul L Houston1,2, Chen Qu3, Qi Yu3
1Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.
The journal of physical chemistry. A
|January 5, 2024
概括
一种新的方法通过使用量子数差异有效地识别并避免计算可以忽略不计的哈密尔顿矩阵元素. 这种计算化学方法补充了机器学习,加速了自值和自函数计算.
科学领域:
- 计算化学是一种计算化学.
- 量子力学就是量子力学.
- 矩阵方法 矩阵方法
背景情况:
- 量子力学的哈密尔顿矩阵通常包含许多可以忽略不计的元素.
- 计算这些元素是计算密集的,阻碍了自身值和自身函数的确定.
- 之前的工作引入了机器学习,用于识别可以忽略的元素.
研究的目的:
- 介绍一种新的,简单的方法,以避免计算可以忽略不计的哈密尔顿矩阵元素.
- 将这种新方法与先前开发的机器学习方法进行比较.
- 探索这两种方法之间的潜在协同作用.
主要方法:
- 使用切断值来计算BRA和KET状态之间的量子数的绝对差异.
- 将该方法应用于H2O,乙烯基基 (C2H3) 和甘氨酸 (C2H5NO2) 的现实的H矩阵.
- 将计算效率和准确性与机器学习分类技术进行比较.
主要成果:
- 新的量子数切断方法有效地避免了计算可以忽略不计的哈密尔顿矩阵元素.
- 该方法在经过测试的案例研究中显示了与机器学习方法可比或更好的性能.
- 确定了量子数切断和机器学习方法之间的显著协同作用.
结论:
- 量子数差异切断为减少哈密尔顿矩阵计算中的计算成本提供了一个高效和简单的替代方案.
- 这种方法为加速量子力学计算提供了有价值的工具.
- 这种方法和机器学习的联合应用可以进一步提高计算效率.
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