数据质量在拟合的近似模型:一个计算化学的视角
Bun Chan1,2, William Dawson2, Takahito Nakajima2
1Graduate School of Engineering, Nagasaki University, Bunkyo 1-14, Nagasaki 852-8521, Japan.
Journal of chemical theory and computation
|November 18, 2024
概括
密度函数理论 (DFT) 配件可以容忍一些低质量的数据,但过量的数据可能会阻碍性能. 使用适度低质量的数据进行多样化可以改善当高质量的数据稀缺时的DFT模型.
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.
- 材料科学是一种材料科学.
背景情况:
- 实证参数化对于密度函数理论 (DFT) 和机器学习 (ML) 模型等量子化学方法至关重要.
- 高质量的数据往往很少,这导致使用低成本,低质量的数据来拟合参数.
研究的目的:
- 调查低质量的数据对DFT方法参数化的影响.
- 为了确定DFT和ML模型的配套中可接受的低质量的数据比例.
主要方法:
- 采用多个不同质量的G2/97数据集的DFT类型方法.
- 分析DFT模型的性能,其高质量数据与低质量数据的比例不同.
- 评估数据多样化对模型准确性的影响.
主要成果:
- 由于DFT的物理基础和有限的参数,DFT配件可以承受相当一部分低质量的数据.
- 将大量低质量的数据添加到小的高质量数据集中可能不会提高性能.
- 当高质量数据有限时,适度的低质量数据可以提高性能.
结论:
- 当超过50%的DFT配套包含可疑数据 (平均误差>20kJ mol-1) 时,请谨慎使用.
- 考虑数据可转移性原则,以确保适合集的多样性,以实现可靠的参数化.
- 在特定条件下,低质量的数据可以对DFT和量子化学ML模型有益.
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