Published March 1, 2023
| Version v2
Dataset
Open
Raw Data for the publication of 'Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals'
Authors/Creators
- 1. Colorado School of Mines
- 2. Florida State University
Description
The dataset used for the ML model described in Machine-Learning of Piezoelectric Coefficients for Wurtzite Crystals.
| Composition |
| ICSD |
| Structure |
| density (gm/cc) |
| a (lattice Parameter a in Angstrom) |
| b (lattice Parameter b in Angstrom) |
| c (lattice Parameter c in Angstrom) |
| z11* (Born Effective charge averaged over all atoms, component 1,1) |
| z22 * (Born Effective charge averaged over al atoms, component 2,2) |
| z33 * (Born Effective charge averaged over al atoms, component 3,3) |
| E11 Dielectric tensor component 11 |
| E22 Dielectric tensor component 11 |
| E33 Dielectric tensor component 11 |
| e15 Piezoelectric stress coefficient tensor component 15 in C/m2 |
| e31 Piezoelectric stress coefficient tensor component 31 in C/m2 |
| e33 Piezoelectric stress coefficient tensor component 33 in C/m2 |
| max-rad in Angstrom |
| min-rad in Angstrom |
| Eneg-max |
| Eneg-min |
| Eneg-diff |
| pol-max |
| pol-min |
| pol-diff |
| C11 Elastic Modulus tensor Cij component 11 in GPa |
| C12 Elastic Modulus tensor Cij component 12 in GPa |
| C13 Elastic Modulus tensor Cij component 13 in GPa |
| C33 Elastic Modulus tensor Cij component 33 in GPa |
| C44 Elastic Modulus tensor Cij component 44 in GPa |
Files
Files
(24.9 kB)
| Name | Size | Download all |
|---|---|---|
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md5:70f41b88b881f99dc5b18da08b90c53b
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