Research data supporting "Predicting novel superconducting hydrides using machine learning approaches"
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Hutcheon, M., Needs, R., & Shipley, A. (2020). Research data supporting "Predicting novel superconducting hydrides using machine learning approaches" [Dataset]. https://doi.org/10.17863/CAM.48347
Crystal structures of the materials for which critical temperatures were calculated in the paper "Predicting novel superconducting hydrides using machine learning approaches" (https://arxiv.org/abs/2001.09852). These crystal structures were generated by selecting low-enthalpy candidates from a random structure search, and performing a geometry optimization at the pressure(s) of interest (the parameters for which are included in each file). The data consists of a set of crystal structure files are named with the following format: a_b_c_d_e_kpts_scf.in where a = the stoichiometry of the material b = the space group of the crystal c = the number of formula units per primitive cell d = pressure at which relaxed e = "primary", or "aux" corresponding to the two different k-point grids used These files are human-readable and contain the crystal lattice specification under the section CELL_PARAMETERS and the atomic positions within the lattice under the ATOMIC_POSITIONS, as well as the various named parameters used in the density functional theory calculations. They may also be read by the quantum-espresso software (https://www.quantum-espresso.org/) or converted to many common crystal-structure formats using the c2x software (https://www.c2x.org.uk/).
The files are provided as quantum-espresso ".in" files. These can be converted to many formats using the C2X software (available here: https://www.c2x.org.uk/).
Crystal structures, Condensed matter, Materials science
Publication Reference: https://doi.org/10.1103/PhysRevB.101.144505
This record's DOI: https://doi.org/10.17863/CAM.48347
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/