高斯过程回归适应密度导向方法:为更大的分子计算潜在能量表面
Denis G Artiukhin1, Ian H Godtliebsen2, Gunnar Schmitz3
1Institut für Chemie und Biochemie, Freie Universität Berlin, Arnimallee 22, 14195 Berlin, Germany.
The Journal of chemical physics
|July 10, 2023
概括
本研究介绍了一种改进的高斯过程回归自适应密度引导方法 (GPR-ADGA),用于高效的潜在能量表面构造. 该方法显著降低了计算成本,同时保持了分子模拟的高精度.
科学领域:
- 计算化学的计算化学
- 理论化学 理论化学
- 量子化学 是一个量子化学.
背景情况:
- 构建精确的潜在能量表面 (PES) 对分子模拟至关重要.
- 传统的方法在计算上可能很昂贵,限制了系统的大小和复杂性.
- 现有的高斯过程回归 (GPR) 方法需要进一步优化效率.
研究的目的:
- 介绍GPR-ADGA的增强实施,以实现自动和成本效益的PES构建.
- 将GPR-ADGA的适用性扩展到更大的分子系统.
- 提高 PES 计算的准确性,降低 PES 计算的计算成本.
主要方法:
- 在MidasCpp程序中实施了改进的高斯过程回归自适应密度引导方法 (GPR-ADGA).
- 采用了 Δ-学习方法,预测了来自和潜力的差异.
- 采用了一种更有效的超参数优化程序.
主要成果:
- 减少了高达80%的单点计算,在基本激发中根平均平方偏差为~3cm-1.
- 实现了低于1厘米-1的误差,收度值更为严格,减少了高达68%的计算.
- 在越来越大的测试分子上展示了性能,并分析了计算墙壁时间.
结论:
- 增强的GPR-ADGA是成本效益高的PES建设的有效工具.
- 该方法可以为更大的分子系统进行高度准确的振动光谱模拟.
- 这种方法显著降低了量子化学计算中的计算负担.
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