6 dataset results for Band Gap

The Materials Project is a collection of chemical compounds labelled with different attributes. The labelling is performed by different simulations, most of them at DFT level of theory.

117 PAPERS • 2 BENCHMARKS

JARVIS-DFT

JARVIS-DFT is a repository of density functional theory based calculation data for materials.

12 PAPERS • 2 BENCHMARKS

Matbench

The Matbench test suite v0.1 contains 13 supervised ML tasks from 10 datasets. Matbench’s data are sourced from various subdisciplines of materials science, such as experimental mechanical properties (alloy strength), computed elastic properties, computed and experimental electronic properties, optical and phonon properties, and thermodynamic stabilities for crystals, 2D materials, and disordered metals. The number of samples in each task ranges from 312 to 132,752, representing both relatively scarce experimental materials properties and comparatively abundant properties such as DFT-GGA formation energies. Each task is a self-contained dataset containing a single material primitive as input (either composition or composition plus crystal structure) and target property as output for each sample.

9 PAPERS • 2 BENCHMARKS

OQMD v1.2 (The Open Quantum Materials Database)

The OQMD is a database of DFT calculated thermodynamic and structural properties of one million materials, created in Chris Wolverton's group at Northwestern University.

3 PAPERS • 4 BENCHMARKS

OQM9HK

This is a large-scale dataset of quantum-mechanically calculated properties (DFT level) of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK). This dataset is constructed on the basis of the Open Quantum Materials Database (OQMD) v1.5 containing more than one million entries, and is the successor to the OQMD v1.2 dataset containing approximately 600k entries (OQM6HK).

2 PAPERS • 3 BENCHMARKS

WBM (Predicting stable crystalline compounds using chemical similarity)

We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data-mining experimental databases. In this way we build 189,981 possible crystal phases, including 18,479 that are on the convex hull of stability. The resulting success rate of 9.72% is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials, and comparable with speed-up factors of machine learning filtering procedures. As a characterization of the set of 18,479 stable compounds, we calculate their electronic band gaps, magnetic moments, and hardness. Our approach, that can be used as a filter on top of any high-throughput scheme, enables us to ef

1 PAPER • NO BENCHMARKS YET

Datasets

6 dataset results for Band Gap