Comparative study of the PBE and SCAN functionals: The particular case of alkali metals

Similarity Clustering for Representative Sets of Inorganic Solids for Density Functional Testing

Benchmarking DFT functionals is complicated since the results highly depend on which properties and materials were used in the process. Unwanted biases can be introduced if a data set contains too many examples of very similar materials. We show that a clustering based on the distribution of density gradient and kinetic energy density is able to identify groups of chemically distinct solids. We then propose a method to create smaller data sets or rebalance existing data sets in a way that no region of the meta-GGA descriptor space is overrepresented, yet the new data set reproduces average errors of the original set as closely as possible. We apply the method to an existing set of 44 inorganic solids and suggest a representative set of seven solids. The representative sets generated with this method can be used to make more general benchmarks or to train new functionals.

Localization in the SCAN meta-generalized gradient approximation functional leading to broken symmetry ground states for graphene and benzene

SCAN over localizes orbitals leading to spin symmetry broken ground states in graphene and benzene.