化学函数的监督学习,使用化分散
Yiwei Liu1, Cheng Zhang2, Zhonghua Liu2
1Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China.
研究人员开发了CF22D,一种新的单一混合功能,为预测各种化学性质提供了更高的准确性. 密度函数理论的进步提高了各种化学系统和反应的预测.
科学领域:
- 计算化学计算化学
- 量子化学 是一个量子化学.
背景情况:
- 科恩-沙姆密度函数理论 (KS-DFT) 是计算化学的一个基石.
- 现有的函数试验很难准确地预测化学性质的全谱.
研究的目的:
- 为广泛的化学应用开发一个高精度的单一混合功能.
- 为了提高现有的非双重混合动力功能的性能.
主要方法:
- 一个灵活的功能形式 (CF22D) 的优化,将全球混合元-不可分离的梯度近似与缓和分散相结合.
- 通过性能触发的代监督学习来培训使用了大型的综合数据库.
主要成果:
- 与大多数现有的非双重混合功能相比,CF22D表现出卓越的全面准确性.
- 通过各种化学基准验证的性能:屏障高度,异构化能,热化学,非共价相互作用,基质/非基质化学和过渡金属化学.
- 对于小型和大型,简单和复杂的系统都有效.
结论:
- CF22D 函数代表了 KS-DFT 化学预测准确度的显著进步.
- 它的灵活设计和严格的优化使它成为计算化学家的一种多功能工具.
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