Kovács Péter, Tran Fabien, Blaha Peter, Madsen Georg K H
Institute of Materials Chemistry, Technical University of Vienna, Getreidemarkt 9/165-TC, A-1060 Vienna, Austria.
J Chem Phys. 2022 Sep 7;157(9):094110. doi: 10.1063/5.0098787.
The space of generalized gradient approximation (GGA) and meta-GGA (mGGA) exchange approximations is systematically explored by training 25 new functionals to produce accurate lattice parameter, cohesive energy, and bandgap predictions. The trained functionals are used to reproduce exact constraints in a data-driven way and to understand the accuracy trade-off between the mentioned properties. The functionals are compared to notable mGGA functionals to analyze how changes in the enhancement factor maps influence the accuracy of predictions. Some of the trained functionals are found to perform on par with specialized functionals for bandgaps, while outperforming them on the other two properties. The error surface of our trained functionals can serve as a soft-limit of what mGGA functionals can achieve.
通过训练25种新的泛函来系统地探索广义梯度近似（GGA）和元广义梯度近似（mGGA）交换近似的空间，以产生准确的晶格参数、结合能和带隙预测。训练后的泛函用于以数据驱动的方式再现精确约束，并理解上述性质之间的精度权衡。将这些泛函与著名的mGGA泛函进行比较，以分析增强因子图的变化如何影响预测精度。发现一些训练后的泛函在带隙方面的表现与专门的泛函相当，而在其他两个性质上则优于它们。我们训练后的泛函的误差曲面可以作为mGGA泛函所能达到的软极限。
