AI Article Synopsis

  • In the last ten years, large databases of material properties have been created, primarily using the widely accepted Perdew-Burke-Ernzerhof (PBE) functional from density-functional theory.
  • Recent theoretical advancements have led to the development of more accurate computational techniques, prompting the creation of a new dataset for 175,000 crystalline materials.
  • This dataset utilizes improved geometry optimization with PBEsol and energy calculations from both PBEsol and SCAN, allowing for reliable predictions of new materials and serving as a resource for machine learning and method comparisons.

Article Abstract

In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for increased accuracy in the calculations. Here, we present a dataset of calculations for 175k crystalline materials obtained with two functionals: geometry optimizations are performed with PBE for solids (PBEsol) that yields consistently better geometries than the PBE functional, and energies are obtained from PBEsol and from SCAN single-point calculations at the PBEsol geometry. Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBEsol, and SCAN.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891291PMC
http://dx.doi.org/10.1038/s41597-022-01177-wDOI Listing

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