Evolutionary algorithms (EAs) coupled with density functional theory (DFT) calculations have been used to predict the most stable hydrides of phosphorus (PHn, n = 1-6) at 100, 150, and 200 GPa. At these pressures phosphine is unstable with respect to decomposition into the elemental phases, as well as PH2 and H2. Three metallic PH2 phases were found to be dynamically stable and superconducting between 100 and 200 GPa. One of these contains five formula units in the primitive cell and has C2/m symmetry (5FU-C2/m). It comprises 1D periodic PH3-PH-PH2-PH-PH3 oligomers. Two structurally related phases consisting of phosphorus atoms that are octahedrally coordinated by four phosphorus atoms in the equatorial positions and two hydrogen atoms in the axial positions (I4/mmm and 2FU-C2/m) were the most stable phases between ∼160-200 GPa. Their superconducting critical temperatures (Tc) were computed as 70 and 76 K, respectively, via the Allen-Dynes modified McMillan formula and using a value of 0.1 for the Coulomb pseudopotential, μ*. Our results suggest that the superconductivity recently observed by Drozdov, Eremets, and Troyan when phosphine was subject to pressures of 207 GPa in a diamond anvil cell may result from these, and other, decomposition products of phosphine.

Good agreement was found between experimental Vickers hardnesses, Hv, of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materials within the AFLOW repository. Because H ML v values can be quickly estimated, they can be used in conjunction with an evolutionary search to predict stable, superhard materials. This methodology is implemented in the XTALOPT evolutionary algorithm. Each crystal is minimized to the nearest local minimum, and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter. Both the energy/enthalpy and H ML v, Teter are employed to determine a structure's fitness. This implementation is applied towards the carbon system, and 43 new superhard phases are found. A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite. arXiv:1906.05886v1 [cond-mat.mtrl-sci]

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Significant progress has been made in the field of a priori crystal structure prediction, with a number of recent remarkable success stories. Herein, we briefly outline the methods that have been developed for finding the global minimum structure and interesting local minima without the need for experimental information. Focus is placed on describing the XtalOpt evolutionary algorithm (EA) developed in our group toward this end. XtalOpt is published under well-known open-source licenses, and the EA searches can be analyzed via the Avogadro chemical editor and visualizer. We describe new algorithmic developments that have made it possible to predict the structures of ever-more complex crystalline lattices. Benchmark tests, which clearly illustrate how the new developments improve the success rate and accelerate the discovery of the global minimum structure, are performed. Finally, we describe how XtalOpt has been employed to predict novel ternary hydrides that have the propensity for high-temperature superconductivity under pressure.

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