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.
Highlights
Background & SummaryThe search for new materials remains one of the most important quests but, one of the great challenges of materials science
Most high throughput searches rely on the use of density functional theory (DFT) within the Perdew-Burke-Ernzerhof (PBE) approximation to the exchange-correlation functional[7]
We have to keep in mind that the complexity of the unit cells increases and that most of the systematic high-throughput DFT studies were performed for ternary systems[37–42]. This can be seen in panel (b) of Fig. 1 where we show a histogram of the number of atoms in the primitive unit cell
Summary
The search for new materials remains one of the most important quests but, one of the great challenges of materials science. After years of data accumulation, there are millions of calculations of materials available in open databases[1,2] that are used as an invaluable reservoir to select and filter promising candidates for further experimental synthesis and characterization These high-throughput studies in solid-state material science[3–6] have broadened the exploration of the vast chemical space, while plenty of works have successfully found and predicted promising materials for technological applications. We used the total energies of all the remaining structures to calculate the convex hulls applying the corrections from the materials project workflow to the energies. From this dataset[23] we selected around 175k compounds that were either stable (i.e., on the convex hull), or close to stable (within 100 meV/atom of the hull). The total energies were reevaluated with SCAN12 to create highly accurate formation energies and convex hulls
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