Note: Times Cited: 875 Reference EPFL-ARTICLE-206025doi:10.1021/cr0501846View record in Web of Science URL: ://WOS:000249839900009 Record created on 2015-03-03, modified on 2017-05-12

Complex Hydrides for Hydrogen Storage

A systematic structure search in the La–H and Y–H systems under pressure reveals some hydrogen-rich structures with intriguing electronic properties. For example, LaH10 is found to adopt a sodalite-like face-centered cubic (fcc) structure, stable above 200 GPa, and LaH8 a C2/m space group structure. Phonon calculations indicate both are dynamically stable; electron phonon calculations coupled to Bardeen–Cooper–Schrieffer (BCS) arguments indicate they might be high-Tc superconductors. In particular, the superconducting transition temperature Tc calculated for LaH10 is 274–286 K at 210 GPa. Similar calculations for the Y–H system predict stability of the sodalite-like fcc YH10 and a Tc above room temperature, reaching 305–326 K at 250 GPa. The study suggests that dense hydrides consisting of these and related hydrogen polyhedral networks may represent new classes of potential very high-temperature superconductors.

Potential high-Tc superconducting lanthanum and yttrium hydrides at high pressure.

Pressure is a fundamental thermodynamic variable that can be used to control the properties of materials, because it reduces interatomic distances and profoundly modifies electronic orbitals and bonding patterns. It is thus a versatile tool for the creation of exotic materials not accessible at ambient conditions. Recently developed static and dynamic high-pressure experimental techniques have led to the synthesis of many functional materials with excellent performance: for example, superconductors, superhard materials and high-energy-density materials. Some of these advances have been aided and accelerated by first-principles crystal-structure searching simulations. In this Review, we discuss recent progress in high-pressure materials discovery, placing particular emphasis on the record high-temperature superconductivity in hydrogen sulfide and on nanotwinned cubic boron nitride and diamond, the hardest known materials. Energy materials and exotic chemical materials obtained under high pressures are also discussed. The main drawback of high-pressure materials is their destabilization after pressure release; this problem and its possible solutions are surveyed in the conclusions, which also provide an outlook on the future developments in the field. High pressure offers a unique degree of freedom for the creation of new materials, leading to new superconductors, superhard materials, high-energy-density materials and exotic chemical materials with unprecedented properties. This Review discusses these materials, along with recently developed theoretical and experimental methods for materials discovery at high pressures.

Materials discovery at high pressures

https://pure.mpg.de/pubman/item/item_3314332_3/component/file_3324953/1-s2.0-S0370157320300363-main.pdf

A perspective on conventional high-temperature superconductors at high pressure: Methods and materials

We demonstrate the application of deep neural networks as a machine-learning tool for the analysis of a large collection of crystallographic data contained in the crystal structure repositories. Using input data in the form of multiperspective atomic fingerprints, which describe coordination topology around unique crystallographic sites, we show that the neural-network model can be trained to effectively distinguish chemical elements based on the topology of their crystallographic environment. The model also identifies structurally similar atomic sites in the entire data set of ∼50000 crystal structures, essentially uncovering trends that reflect the periodic table of elements. The trained model was used to analyze templates derived from the known crystal structures in order to predict the likelihood of forming new compounds that could be generated by placing elements into these structural templates in a combinatorial fashion. Statistical analysis of predictive performance of the neural-network model, whi...

Crystal Structure Prediction via Deep Learning

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.

Decomposition Products of Phosphine Under Pressure: PH2 Stable and Superconducting?

The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, 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 $$H_{\mathrm{v}}^{{\mathrm{ML}}}$$
 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 $$H_{{\mathrm{v,Teter}}}^{{\mathrm{ML}}}$$
 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.

/pdf/predicting-superhard-materials-via-a-machine-learning-2nkf9ebcs2.pdf

Predicting superhard materials via a machine learning informed evolutionary structure search

Evolutionary algorithms (EA) coupled with Density Functional Theory (DFT) calculations have been used to predict the most stable hydrides of phosphorous (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 100-200 GPa. One of these contains five formula units in the primitive cell and has C2/m symmetry (5FU-C2/m). It is comprised of 1D periodic PH3-PH-PH2-PH-PH3 oligomers. Two structurally related phases consisting of phosphorous atoms that are octahedrally coordinated by four phosphorous 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 being 70 and 76 K, respectively, via the Allen-Dynes modified McMillan formula and using a value of 0.1 for the Coulomb pseudopotential, u*. 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.

XTALOPT version r9: An open-source evolutionary algorithm for crystal structure prediction

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 d...

Patrick Avery

Papers

Decomposition Products of Phosphine Under Pressure: PH2 Stable and Superconducting?

Predicting superhard materials via a machine learning informed evolutionary structure search

Decomposition Products of Phosphine Under Pressure: PH2 Stable and Superconducting?

XTALOPT version r9: An open-source evolutionary algorithm for crystal structure prediction

The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction