Designing Small, Representative Databases for Density Functional Testing and Electronic Structure Validation

Abstract

Density Functional Theory (DFT) is widely used in computational chemistry and condensed matter physics to predict electronic structures and material properties. The accuracy of DFT heavily depends on the choice of exchange-correlation functionals, which necessitates extensive validation against high-fidelity reference data. Constructing large-scale reference datasets can be computationally prohibitive, necessitating the development of small yet representative databases for efficient functional testing and electronic structure validation. This research explores strategies for designing such databases, focusing on the selection of diverse molecular and solid-state systems, optimization techniques to minimize redundancy, and metrics to ensure robustness in functional assessment. A thorough analysis is conducted on various selection methodologies, including clustering techniques, feature-space sampling, and error minimization approaches. Experimental validation using well-established functionals, such as PBE, B3LYP, and SCAN, demonstrates that carefully curated small datasets can provide reliable benchmarking comparable to larger databases. The results highlight that systematic selection criteria, coupled with domain knowledge, can significantly enhance the efficiency of functional development and validation. The study contributes to the field by proposing a framework for constructing compact yet reliable datasets that can expedite computational workflows without sacrificing accuracy.