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MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation
Journal of Computational Chemistry ( IF 3 ) Pub Date : 2021-08-18 , DOI: 10.1002/jcc.26732
Kristopher Brown 1, 2 , Yasheng Maimaiti 1, 2 , Kai Trepte 1 , Thomas Bligaard 3 , Johannes Voss 1
Affiliation  

The predictive power of density functional theory for materials properties can be improved without increasing the overall computational complexity by extending the generalized gradient approximation (GGA) for electronic exchange and correlation to density functionals depending on the electronic kinetic energy density in addition to the charge density and its gradient, resulting in a meta-GGA. Here, we propose an empirical meta-GGA model that is based both on physical constraints and on experimental and quantum chemistry reference data. The resulting optimized meta-GGA MCML yields improved surface and gas phase reaction energetics without sacrificing the accuracy of bulk property predictions of existing meta-GGA approaches.

中文翻译:

MCML:将物理约束与实验数据相结合,实现多用途元广义梯度近似

密度泛函理论对材料特性的预测能力可以通过扩展广义梯度近似 (GGA) 用于电子交换和与密度泛函的相关性来提高,而不会增加整体计算复杂性,这取决于电子动能密度以及电荷密度和它的梯度,产生了一个元 GGA。在这里,我们提出了一个基于物理约束以及实验和量子化学参考数据的经验元 GGA 模型。由此产生的优化的元 GGA MCML 产生了改进的表面和气相反应能量学,而不会牺牲现有元 GGA 方法的体积特性预测的准确性。
更新日期:2021-09-09
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