机器学习分类可以显著降低在CI计算中计算哈密尔顿矩阵的成本
Chen Qu1, Paul L Houston2, Qi Yu3
1Independent Researcher, Toronto, Ontario M9B 0E3, Canada.
The Journal of chemical physics
|August 16, 2023
概括
计算哈密尔顿矩阵在计算上是昂贵的. 机器学习可以预测这些矩阵中的零元素,大大降低了分子模拟的计算成本.
科学领域:
- 计算化学是一种计算化学.
- 量子化学是一种量子化学.
- 分子动力学分子动力学
背景情况:
- 哈密尔顿矩阵对于电子和核计算至关重要,但在计算上是密集的.
- 这些矩阵往往包含许多小的非对角元素,这给计算带来了挑战.
研究的目的:
- 通过将小非对角元素设置为零来研究近似哈密尔顿矩阵的可行性.
- 探索机器学习方法,以有效地预测这些零元素.
主要方法:
- 利用了水 (H2O),乙烯 (C2H3) 和甘氨酸 (C2H5NO2) 的全尺寸ab initio潜在表面.
- 经验性地确定了将小非对角线元素归零对自身值准确性的影响.
- 应用了三种机器学习模型来预测哈密尔顿矩阵中的零元素.
主要成果:
- 用合适的值将小非对角线元素归零会导致自身值的最小误差.
- 机器学习模型在训练一小部分计算值后成功预测了零元素.
- 对于乙烯基和甘氨酸,在~1%的元素上进行训练,产生了具有~2cm-1.0的平均绝对误差的自值.
结论:
- 通过将小非对角线元素归零来对哈密尔顿矩阵进行近似是一个可行的策略.
- 机器学习提供了一种有效的方法来预测零元素,降低量子化学中的计算成本.
- 这种方法显示出加速大规模分子模拟的前景.
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