Efficient Benchmark Databases for Evaluating Density Functionals and Computational Chemistry Methods

Abstract

Benchmark databases play a crucial role in evaluating the accuracy and efficiency of computational chemistry methods, particularly density functional theory (DFT). With the continuous expansion of computational methodologies, it becomes imperative to develop standardized, high-quality datasets that ensure the reliability of theoretical predictions. Benchmark databases serve as essential tools for validating and comparing density functionals, helping researchers determine the most appropriate methods for specific applications. However, constructing efficient benchmark databases requires a careful balance between accuracy, computational cost, and representativeness of chemical diversity. This paper discusses the principles of constructing efficient benchmark databases, highlights key challenges, and presents experimental results demonstrating their effectiveness. Our findings indicate that well-curated benchmark datasets significantly improve the predictive capabilities of computational chemistry methods and facilitate methodological advancements.