局部混合功能,由神经网络和强相关模型构建的复合局部混合功能
Artur Wodyński1, Martin Kaupp1
1Institute of Chemistry, Theoretical Chemistry/Quantum Chemistry, Sekr. C7, Technische Universität Berlin, Berlin, Germany.
Journal of computational chemistry
|January 24, 2026
概括
局部混合函数 (LHs) 与神经网络局部混合函数 (n-LMFs) 现在有效地平衡自我相互作用和静态相关错误. 新的LH25nP函数实现了主要组能量的创纪录的准确性,并改善了与旋转相关的化学问题.
科学领域:
- 计算化学计算化学
- 量子化学 是一个量子化学.
- 材料科学 材料科学 材料科学
背景情况:
- 局部混合函数 (LHs) 提供了一种灵活的方法来平衡密度函数近似中的自我相互作用和静态相关误差.
- 最近的进展将强相关系因子纳入局部混合函数 (LMF) 中,以提高性能.
- 机器学习已经被用来开发神经网络的LMF (n-LMFs),显示主要组能量学的希望.
研究的目的:
- 开发一种新的局部混合功能,结合神经网络局部混合功能,优化强相关效应.
- 为了评估新函数的性能,LH25nP,主要组的能量和反应能量.
- 评估函数在各种化学系统中解决与旋转有关的问题的能力.
主要方法:
- 开发LH25nP函数,其中包括一个神经网络局部混合函数 (n-LMF),并以固定的强相关系因子进行优化.
- 严格测试使用GMTKN55基准集用于主组能量和W4-11RE集用于反应能量.
- 分数旋转错误的评估和旋转限制债券解离和旋转污染问题的性能.
主要成果:
- 在GMTKN55上,LH25nP-D4在GMTKN55上实现了创纪录的低自相一致的WTMAD-2值2.47 kcal/mol,这对于4级功能来说是最好的.
- 函数证明了4级函数的W4-11RE反应能量的最小平均绝对偏差 (2.4 kcal/mol).
- 在分数旋转误差,共价键的旋转受限解离以及过渡金属复合体中的旋转污染问题上观察到显著的改善.
结论:
- LH25nP函数代表了局部混合函数的显著进步,有效地减轻了自我相互作用和静态相关性错误.
- 它在基准数据集和与旋转相关的问题上的表现表明它偏离了功能开发中的典型权衡.
- 建议进一步优化,包括过渡金属数据,以提高对有机金属系统的可转移性.
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