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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Elasticity tensors of 10276 crystals from DFT computations

Authors: Mingjian Wen; Matthew K. Horton; Jason M. Munro; Patrick Huck; Kristin A. Persson;

Elasticity tensors of 10276 crystals from DFT computations

Abstract

Paper introducing this dataset Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Digital Discovery. DOI:https://doi.org/10.1039/D3DD00233K This dataset consists of three data files in the json format. Each file is explained below. crystal_elasticity_tensor.json DFT computed elastic tensors of 10276 crystals used for developing the MatTen model. structure: crystal structure of the materialformula_pretty: chemical formulacrystal_system: crystal systemelastic_tensor: full fourth-rank elastic tensorelastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensorsplit: split of the data into train, validation, and test subsets for model development max_directional_E.json New crystals with large maximum directional Young's modulus. material_id: Materials Project identifierformula_pretty: chemical formula structure_original: crystal structure from the Materials Project databaseelastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original`max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original` structure: further DFT optimized structure with a tigher criterionelastic_tensor: DFT elastic tensor corresponding to `structure`max_directional_E: DFT maximum directional Young's modulus using `structure`elastic_tensor_matten: MatTen predicted elastic tensor using `structure`max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure` elemental_cubic_metal_max_E_along_100_direction.json New crystals with its maximum directional Young's modulus along the [100] direction. material_id: Materials Project identifierformula_pretty: chemical formula structure_original: crystal structure from the Materials Project databaseelastic_tensor_matten: MatTen predicted elastic tensor using `structure_original`Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten` structure: further DFT optimized structure with a tigher criterionelastic_tensor: DFT elastic tensor corresponding to `structure`Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`

Related Organizations
Keywords

Materials informatics, Elasticity tensors, Machine learning, Materials properties, Graph neural networks

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average