460962 A Combinatorial Approach for Developing Minimally-Parameterized and Highly-Transferable Density Functionals
Since semi-empirical density functionals utilize power series enhancement factors, the systematic optimization of a density functional can be imagined as a combinatorial search problem, with all possible combinations of the available power series variables serving as potential functional forms. This approach increases the number of candidate functional forms from just a handful to 2n-1, where n is the total number of available variables. With the significantly enlarged functional space, fits can be characterized not only based on their training set performance, but additionally with respect to their transferability to an independent test set as well as their physical characteristics.
This procedure has recently been used to partially explore a meta-GGA functional space of 1041 possible functional forms, resulting in the development of ωB97M-V, a range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation. With only 12 linear parameters, the functional significantly outperforms methods in and below its class that have upwards of 50 linear parameters on non-covalent interactions, isomerization energies, thermochemistry, and barrier heights. The merits of ωB97M-V will be demonstrated by comparing it to leading density functionals (such as M06-2X and M11) across a database of nearly 5,000 data points.
See more of this Group/Topical: Computational Molecular Science and Engineering Forum