2019
DOI: 10.1088/1367-2630/aaf751
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Main-group test set for materials science and engineering with user-friendly graphical tools for error analysis: systematic benchmark of the numerical and intrinsic errors in state-of-the-art electronic-structure approximations

Abstract: Understanding the applicability and limitations of electronic-structure methods needs careful and efficient comparison with accurate reference data. Knowledge of the quality and errors of electronicstructure calculations is crucial to advanced method development, high-throughput computations and data analyses. In this paper, we present a main-group test set for computational materials science and engineering (MSE), that provides accurate and easily accessible crystal properties for a hierarchy of exchange-corr… Show more

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Cited by 21 publications
(22 citation statements)
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“…The veracity of computational data is thus dominated by bias—offset from the experimental ground truth. The estimation of this bias depends on access to experimental data or comparison to results from a higher‐level theory . The bias also depends on the types of chemical elements in the materials.…”
Section: Data Qualitymentioning
confidence: 99%
“…The veracity of computational data is thus dominated by bias—offset from the experimental ground truth. The estimation of this bias depends on access to experimental data or comparison to results from a higher‐level theory . The bias also depends on the types of chemical elements in the materials.…”
Section: Data Qualitymentioning
confidence: 99%
“…This has been prohibitively expensive until recently, but new algorithms and hardware have made some benchmark calculations possible. [55][56][57] In this context, highly accurate (sub-kJ mol À1 ) lattice energy predictions have been demonstrated, e.g. by Yang et al 43 via a fragment strategy and by Zen et al via diffusion quantum Monte Carlo.…”
Section: Crystal Structure Prediction Beyond Density Functional Theorymentioning
confidence: 99%
“…Furthermore, the field urgently needs benchmarks for the various numerical approximations and for exchangecorrelations potentials in order to address also accuracy, not only numerical precision. The MSE (materials science and engineering) project is a promising step in this direction (Zhang et al 2019). Without all this, data-driven science will be limited in its capabilities.…”
Section: Discussionmentioning
confidence: 99%