在量子化学的 Δ-ML 和多忠实性模型中对数据效率进行基准测试
1School of Mathematics and Natural Sciences, University of Wuppertal, Gaussstrasse 20, 42117 Wuppertal, Germany.
新的机器学习 (ML) 方法可以降低量子化学 (QC) 的成本. 多忠实性方法,包括新的MFΔML,在预测分子性质方面比标准的Δ-ML具有优势,特别是在大型数据集中.
科学领域:
- 计算化学是一种计算化学.
- 机器学习应用程序 机器学习应用程序
- 量子力学就是量子力学.
背景情况:
- 机器学习 (ML) 显著降低了量子化学 (QC) 中的计算成本.
- 在QC中生成ML的培训数据仍然是相当大的成本.
- 多忠实机器学习 (MFML) 方法利用来自多个QC准确度级别的数据来降低成本.
研究的目的:
- 为了比较QC的各种ML方法的数据生成成本.
- 评估 Δ-ML,MFML,优化 MFML 和一个新的 MFΔML 方法.
- 为了评估基本状态能量,激发能量和二极子时刻的预测准确性.
主要方法:
- 使用了多忠实基准数据集QeMFi.
- 对 Δ-ML,MFML,优化 MFML 和 MFΔML 的数据成本进行比较.
- 与单一忠诚度内核回归进行基准测试.
主要成果:
- 多忠实度方法在许多预测中表现优于标准 Δ-ML.
- 新的MFΔML方法显示,对有限数量的预测有优势.
- 对于QC中的特定ML应用,MFΔML提供了一种具有成本效益的方法.
结论:
- 对于大规模的QC预测,MFML策略优于Δ-ML.
- 对于需要很少评估的ML模型,MFΔML方法提供了一个有价值的替代方案.
- 优化数据生成成本对于高效的ML驱动的QC至关重要.
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