一个接近黑盒子的参数优化器用于NDDO-后代半实证方法:MNDO和AM1的演示
Adrian Wee Wen Ong1, Steve Yueran Cao1, Leemen Chee Yong Chan1
1Centre for Quantum Technologies, National University of Singapore, Singapore 117543, Singapore.
The journal of physical chemistry. A
|December 22, 2025
概括
本研究介绍了一种改进的算法,用于优化计算化学中的分子几何学,直接使用几何导数. 增强方法对MNDO和AM1模型的参数进行了改进,提高了键和角度预测的准确性.
科学领域:
- 计算化学计算化学
- 理论化学 理论化学
- 量子化学 是一个量子化学.
背景情况:
- 半经验量子化学方法,如二原子重叠的修改忽视 (MNDO) 和奥斯模型1 (AM1),被广泛用于分子建模.
- 参数优化对于提高这些方法的准确性至关重要.
- 像PARAM这样的现有方法依赖于衍生几何函数,这些函数可能有局限性.
研究的目的:
- 介绍一个增强的几何校正参数优化算法.
- 直接将几何参数 (债券长度,角度) 的导数纳入优化过程中.
- 通过MNDO和AM1模型的重构参数化来证明算法的实用性.
主要方法:
- 开发了一个增强的算法,直接使用关键长度和角度的导数.
- 应用了算法来重新参数化MNDO和AM1模型,使用1187个CHNO分子的数据集.
- 将结果与传统的PARAM计划进行比较,并检查了培训/验证子集.
主要成果:
- 增强的AM1重定量化在预测的几何性质 (债券长度,角度,二面角) 中显示出了实质性的改进.
- 在 PARAM 中使用的衍生几何参考函数被发现不适合参数优化.
- 观察到反应屏障高度的精度下降,可能是由于排除了不平衡几何.
结论:
- 与现有方法相比,新型算法在预测分子几何学方面提供了显著的改进.
- 使用新算法进行明智的参数精细化,可以大大提高NDDO后代半实证模型的性能.
- 这些发现表明,目前使用衍生几何函数进行参数优化的方法存在局限性.
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