Acknowledging user requirements for accuracy in computational chemistry benchmarks

2020/04/01 by Andreas Savin, Savin, Andreas, Pascal Pernot +1 · 1 citation
Computer Science · Decision Sciences · Materials Science · #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #Data Analysis #FOS: Physical sciences #Machine Learning in Materials Science #Scientific Computing and Data Management #Statistics and Probability (physics.data-an)

paper · doi:10.48550/arxiv.2004.00297

openalex publication_date 2020/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Computational chemistry has become an important complement to experimental measurements. In order to choose among the multitude of the existing approximations, it is common to use benchmark data sets, and to issue recommendations based on numbers such as mean absolute errors. We argue, using as an example band gaps calculated with density functional approximations, that a more careful study of the benchmark data is needed, stressing that the user's requirements play a role in the choice of an appropriate method. We also appeal to those who measure data capable of being used as a reference, to publish error estimates. We show how the latter can affect the judgment of approximations used in computational chemistry.

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