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.

Automated summary

Benchmarking DFT functionals can be complex due to the dependence of results on properties and materials used. A clustering approach based on density gradient and kinetic energy density distribution can identify chemically distinct solids. The proposed method aims to create smaller or rebalanced data sets to accurately replicate average errors of the original set, which can be applied to make general benchmarks or train new functionals.