Research data supporting "Predicting novel superconducting hydrides using machine learning approaches"
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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/).
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Engineering and Physical Sciences Research Council (EP/L015552/1)
Engineering and Physical Sciences Research Council (EP/P022596/1)