Good Practices in Database Generation for Benchmarking Density Functional Theory

IF 16.8 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Amir Karton, Marcelo T. de Oliveira
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引用次数: 0

Abstract

The hundreds of density functional theory (DFT) methods developed over the past three decades are often referred to as the “zoo” of DFT approximations. In line with this terminology, the numerous DFT benchmark studies might be considered the “safari” of DFT evaluation efforts, reflecting their abundance, diversity, and wide range of application and methodological aspects. These benchmarks have played a critical role in establishing DFT as the dominant approach in quantum chemical applications and remain essential for selecting an appropriate DFT method for specific chemical properties (e.g., reaction energy, barrier height, or noncovalent interaction energy) and systems (e.g., organic, inorganic, or organometallic). DFT benchmark studies are a vital tool for both DFT users in method selection and DFT developers in method design and parameterization. This review provides best-practice guidance on key methodological aspects of DFT benchmarking, such as the quality of benchmark reference values, dataset size, reference geometries, basis sets, statistical analysis, and electronic availability of the benchmark data. Additionally, we present a flowchart to assist users in systematically choosing these methodological aspects, thereby enhancing the reliability and reproducibility of DFT benchmarking studies.

Abstract Image

密度泛函理论基准数据库生成的良好实践
在过去的三十年中,密度泛函理论(DFT)的数百种方法经常被称为DFT近似的“动物园”。根据这个术语,大量的DFT基准研究可以被认为是DFT评估工作的“游猎”,反映了它们的丰富性、多样性以及广泛的应用和方法方面。这些基准在建立DFT作为量子化学应用的主导方法方面发挥了关键作用,并且对于特定化学性质(例如,反应能,势垒高度或非共价相互作用能)和系统(例如,有机,无机或有机金属)选择适当的DFT方法仍然至关重要。DFT基准研究是DFT使用者选择方法和DFT开发者设计方法和参数化方法的重要工具。本综述提供了关于DFT基准测试的关键方法方面的最佳实践指导,例如基准参考值的质量、数据集大小、参考几何形状、基础集、统计分析和基准数据的电子可用性。此外,我们提出了一个流程图,以帮助用户系统地选择这些方法学方面,从而提高DFT基准研究的可靠性和可重复性。
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来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
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