Possible high-temperature superconductors predicted from electronic structure and data-filtering algorithms

We report here the completion of the electronic structure of the majority of the known stoichiometric inorganic compounds, as listed in the International Crystal Structure Data-base (ICSD). We make a detailed comparison of the electronic structure, crystal geometry and chemical bonding of cuprate high temperature superconductors, with the calculated over sixty thousand electronic structures. Based on compelling similarities of the electronic structures in the normal state and a data-filtering technique, we propose that high temperature superconductivity is possible for electron- or hole-doping in a much larger group of materials than previously considered. The indentified materials are composed of over one hundred layered compounds, most which hitherto are untested with respect to their super conducting properties. Of particular interest are the following materials; Ca$_2$(CuBr$_2$O$_2$), K$_2$CoF$_4$, Sr$_2$(MoO$_4$) and Sr$_4$V$_3$O$_{10}$, which are discussed in detail.

The search for strong topological insulators

Topological insulators [1-6] is a new quantum phase of matter with exotic properties such as dissipationless transport and protection against Anderson localization [7]. These new states of quantum matter could be one of the missing links for the realization of quantum computing [8,9] and will probably result in new spintronic or magnetoelectric devices. Moreover, topological insulators will be a strong competitor with graphene in electronic application. Because of these potential application the topological insulator research has literally exploded during the last year. Motivated by the fact that up-to-date only few 3D systems are identified to belong to this new quantum phase [10-18] we have used massive computing in combination with data-mining to search for new strong topological insulators. In this letter we present a number of non-layered compounds that show band inversion at the $Gamma$-point, a clear signal of a strong topological insulator.

A theoretical analysis of the chemical bonding and electronic structure of graphene interacting with Group IA and Group VIIA elements

We propose a new class of materials, which can be viewed as graphene derivatives involving Group IA or Group VIIA elements, forming what we refer to as graphXene. We show that in several cases large band gaps can be found to open up, whereas in other cases a semimetallic behavior is found. Formation energies indicate that under ambient conditions, sp$^3$ and mixed sp$^2$/sp$^3$ systems will form. The results presented allow us to propose that by careful tuning of the relative concentration of the adsorbed atoms, it should be possible to tune the band gap of graphXene to take any value between 0 and 6.4 eV.

Accurate electronic band gap of pure and functionalized graphane from GW calculations

Using the GW approximation, we study the electronic structure of the recently synthesized hydrogenated graphene, named graphane. For both conformations, the minimum band gap is found to be direct at the $Gamma$ point, and it has a value of 5.4 eV in the stable chair conformation, where H atoms attach C atoms alternatively on opposite sides of the two dimensional carbon network. In the meta-stable boat conformation the energy gap is 4.9 eV. Then, using a supercell approach, the electronic structure of graphane was modified by introducing either an hydroxyl group or an H vacancy. In this last case, an impurity state appears at about 2 eV above the valence band maximum.

Data mining and accelerated electronic structure theory as a tool in the search for new functional materials

Data mining is a recognized predictive tool in a variety of areas ranging from bioinformatics and drug design to crystal structure prediction. In the present study, an electronic structure implementation has been combined with structural data from the Inorganic Crystal Structure Database to generate results for highly accelerated electronic structure calculations of about 22,000 inorganic compounds. It is shown how data mining algorithms employed on the database can identify new functional materials with desired materials properties, resulting in a prediction of 136 novel materials with potential for use as detector materials for ionizing radiation. The methodology behind the automatized ab-initio approach is presented, results are tabulated and a version of the complete database is made available at the internet web site http://gurka.fysik.uu.se/ESP/ (Ref.1).