几何优化:不同开源几何优化器的比较
Akhil Shajan1, Madushanka Manathunga1,2, Andreas W Götz3
1Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.
Journal of chemical theory and computation
|October 23, 2023
概括
与笛卡尔坐标相比,内部坐标加速了分子几何优化. 机器学习和特定的算法,如ASE/Berny和ASE/Sella显示出对高效的能源最小化的承诺.
科学领域:
- 计算化学的计算化学
- 分子建模分子建模
- 量子化学 是一个量子化学.
背景情况:
- 准确的分子几何学对于理解化学性质和反应至关重要.
- 有效的几何优化算法对于计算化学至关重要.
- 评估开源工具对于可访问性和可重复性至关重要.
研究的目的:
- 为了比较各种与量子交互计算内核 (QUICK) 接口的开源几何优化代码的性能.
- 评估坐标系 (内部与笛卡尔) 对优化效率的影响.
- 为了评估一个机器学习方法 (高斯过程回归) 的能量最小化.
主要方法:
- 在30个有机分子的贝克测试集上进行的能量最小化.
- 几何优化代码的比较,包括geomeTRIC,DL-FIND和ASE (使用伯尼和塞拉算法).
- 使用 QUICK 进行梯度和能量计算,使用 Hartree-Fock 理论和 6-31G** 基准设置进行机器学习评估.
主要成果:
- 内部坐标总是需要比笛卡尔坐标更少的优化步骤.
- 选择最初的赫森和更新方法显著影响了趋同率.
- 高斯过程回归 (GPR) 模型在内部坐标方面表现出卓越的性能.
- 与其他测试方法相比,ASE/Berny和ASE/Sella显示出更快的趋同.
结论:
- 建议使用内部坐标来更快地优化几何.
- ASE/Berny和ASE/Sella适用于大规模的优化任务.
- 与DL-FIND集成的QUICK提供了一个可行的单个可执行的优化解决方案.
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