The Materials Project is a collection of chemical compounds labelled with different attributes.
84 PAPERS • 2 BENCHMARKS
QM9 provides quantum chemical properties for a relevant, consistent, and comprehensive chemical space of small organic molecules. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships.
44 PAPERS • 5 BENCHMARKS
JARVIS-DFT is a repository of density functional theory based calculation data for materials.
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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.
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.
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This is a large-scale dataset of quantum-mechanically calculated properties 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).
1 PAPER • 3 BENCHMARKS