| 1. |
- Borysov, Stanislav S., et al.
(författare)
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Online search tool for graphical patterns in electronic band structures
- 2018
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Ingår i: npj Computational Materials. - : Springer Science and Business Media LLC. - 2057-3960. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Many functional materials can be characterized by a specific pattern in their electronic band structure, for example, Dirac materials, characterized by a linear crossing of bands; topological insulators, characterized by a Mexican hat pattern or an effectively free electron gas, characterized by a parabolic dispersion. To find material realizations of these features, manual inspection of electronic band structures represents a relatively easy task for a small number of materials. However, the growing amount of data contained within modern electronic band structure databases makes this approach impracticable. To address this problem, we present an automatic graphical pattern search tool implemented for the electronic band structures contained within the Organic Materials Database. The tool is capable of finding user-specified graphical patterns in the collection of thousands of band structures from high-throughput calculations in the online regime. Using this tool, it only takes a few seconds to find an arbitrary graphical pattern within the ten electronic bands near the Fermi level for 26,739 organic crystals. The source code of the developed tool is freely available and can be adapted to any other electronic band structure database.
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| 2. |
- Hafiz, Hasnain, et al.
(författare)
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A high-throughput data analysis and materials discovery tool for strongly correlated materials
- 2018
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Ingår i: npj Computational Materials. - : Springer Science and Business Media LLC. - 2057-3960. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Modeling of f-electron systems is challenging due to the complex interplay of the effects of spin-orbit coupling, electron-electron interactions, and the hybridization of the localized f-electrons with itinerant conduction electrons. This complexity drives not only the richness of electronic properties but also makes these materials suitable for diverse technological applications. In this context, we propose and implement a data-driven approach to aid the materials discovery process. By deploying state-of-the-art algorithms and query tools, we train our learning models using a large, simulated dataset based on existing actinide and lanthanide compounds. The machine-learned models so obtained can then be used to search for new classes of stable materials with desired electronic and physical properties. We discuss the basic structure of our f-electron database, and our approach towards cleaning and correcting the structure data files. Illustrative examples of the applications of our database include successful prediction of stable superstructures of double perovskites and identification of a number of physically-relevant trends in strongly correlated features of f-electron based materials.
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| 3. |
- Ivády, Viktor, et al.
(författare)
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First principles calculation of spin-related quantities for point defect qubit research
- 2018
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Ingår i: npj Computational Materials. - : SPRINGERNATURE. - 2057-3960. ; 4
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Forskningsöversikt (refereegranskat)abstract
- Point defect research in semiconductors has gained remarkable new momentum due to the identification of special point defects that can implement qubits and single photon emitters with unique characteristics. Indeed, these implementations are among the few alternatives for quantum technologies that may operate even at room temperature, and therefore discoveries and characterization of novel point defects may highly facilitate future solid state quantum technologies. First principles calculations play an important role in point defect research, since they provide a direct, extended insight into the formation of the defect states. In the last decades, considerable efforts have been made to calculate spin-dependent properties of point defects from first principles. The developed methods have already demonstrated their essential role in quantitative understanding of the physics and application of point defect qubits. Here, we review and discuss accuracy aspects of these novel ab initio methods and report on their most relevant applications for existing point defect qubits in semiconductors. We pay attention to the advantages and limitations of the methodological solutions and highlight additional developments that are expected in the near future. Moreover, we discuss the opportunity of a systematic search for potential point defect qubits, as well as the possible development of predictive spin dynamic simulations facilitated by ab initio calculations of spin-dependent quantities.
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