Reproducible density functional theory predictions of bandgaps for materials

Even though reproducible computational procedures for density-functional-theory (DFT) calculations of molecular properties are well established, the additional complexities for calculations of materials properties present significant current issues. Considering a randomly selected set of 340 3D materials, we demonstrate that standard computational protocols lead to c.a. 20% occurrences of significant failures during bandgap calculations. The bandgap is a quintessential materials property that underpins the prediction of most other properties. Examined herein are the effects of the choice of the pseudopotential to describe core electrons, the plane-wave basis-set cutoff energy, and the Brillouin-zone integration. For the pseudopotential and the cutoff energy, optimization of internal computational parameters is performed. For the Brillouin-zone integration, a new computational protocol is developed that chooses grids by minimization of interpolation errors using the second-derivative matrix of the orbital energies. This is shown to provide significant enhancement over established procedures that seek merely to maximize integration-grid densities.

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Publication Details

Journal
Computational Condensed Matter
Published
2025-08-29
DOI
https://doi.org/10.1016/j.cocom.2025.e01122
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Reproducible density functional theory predictions of bandgaps for materials

Roger D. Amos, Michael J. Ford, Rika Kobayashi, Jeffrey R. Reimers et al.
1 citations
Computational Condensed Matter
Machine Learning in Materials Science
0.52
article

Reproducible density functional theory predictions of bandgaps for materials

Roger D. Amos, Michael J. Ford, Rika Kobayashi, Jeffrey R. Reimers, Musen Li, Chenxi Lu
article en
1 citations

Abstract

Even though reproducible computational procedures for density-functional-theory (DFT) calculations of molecular properties are well established, the additional complexities for calculations of materials properties present significant current issues. Considering a randomly selected set of 340 3D materials, we demonstrate that standard computational protocols lead to c.a. 20% occurrences of significant failures during bandgap calculations. The bandgap is a quintessential materials property that underpins the prediction of most other properties. Examined herein are the effects of the choice of the pseudopotential to describe core electrons, the plane-wave basis-set cutoff energy, and the Brillouin-zone integration. For the pseudopotential and the cutoff energy, optimization of internal computational parameters is performed. For the Brillouin-zone integration, a new computational protocol is developed that chooses grids by minimization of interpolation errors using the second-derivative matrix of the orbital energies. This is shown to provide significant enhancement over established procedures that seek merely to maximize integration-grid densities.

Computational Condensed MatterVol. 45
Shanghai University (CN), University of Technology Sydney (AU), University of Canberra (AU), IP Australia (AU)
National Computational Infrastructure, University of Technology Sydney, National Natural Science Foundation of China, China Postdoctoral Science Foundation, Australian Research Council, Young Scientists Fund
Openalex Percentile: Top 34%
Machine Learning in Materials Science
0.52
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Reproducible density functional theory predictions of bandgaps for materials — Roger D. Amos, Michael J. Ford, et al. · Computational Condensed Matter (2025) | TGRS Research Map | TGRS