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Register a new userData mining and accelerated electronic structure theory as a tool in the search for new functional materials
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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).
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The strain effect on electronic structure and thermoelectric properties of Higher Manganese Silicides (HMSs) Mn4Si7 was studied using Density Functional Theory (DFT) and through solving Boltzman Transport Equation (BTE). We found that the tensile strain attempts to reduce the band gap while the compressive strain not much affect to band gap. The Seebeck coeficient was found to be increased with increasing temperature, which is very consistent to experiments. The electrical conductivity and power factor show highly degree of anisotropy, where in-plane direction is more dominant. The different behavior of electrical conductivity along in-plane and outof plane direction was explained due to the change of band dispersion in the valence band maximum (VBM).
In this paper we utilize techniques from the theory of non-linear dynamical systems to define a notion of embedding threshold estimators. More specifically we use delay-coordinates embeddings of sets of coefficients of the measured signal (in some chosen frame) as a data mining tool to separate structures that are likely to be generated by signals belonging to some predetermined data set. We describe a particular variation of the embedding threshold estimator implemented in a windowed Fourier frame, and we apply it to speech signals heavily corrupted with the addition of several types of white noise. Our experimental work seems to suggest that, after training on the data sets of interest,these estimators perform well for a variety of white noise processes and noise intensity levels. The method is compared, for the case of Gaussian white noise, to a block thresholding estimator.
The electronic structure of surfaces plays a key role in the properties of quantum devices. However, surfaces are also the most challenging to simulate and engineer. Here, we study the electronic structure of InAs(001), InAs(111), and InSb(110) surfaces using a combination of density functional theory (DFT) and angle-resolved photoemission spectroscopy (ARPES). We were able to perform large-scale first principles simulations and capture effects of different surface reconstructions by using DFT calculations with a machine-learned Hubbard U correction [npj Comput. Mater. 6, 180 (2020)]. To facilitate direct comparison with ARPES results, we implemented a bulk unfolding scheme by projecting the calculated band structure of a supercell surface slab model onto the bulk primitive cell. For all three surfaces, we find a good agreement between DFT calculations and ARPES. For InAs(001), the simulations clarify the effect of the surface reconstruction. Different reconstructions are found to produce distinctive surface states. For InAs(111) and InSb(110), the simulations help elucidate the effect of oxidation. Owing to larger charge transfer from As to O than from Sb to O, oxidation of InAs(111) leads to significant band bending and produces an electron pocket, whereas oxidation of InSb(110) does not. Our combined theoretical and experimental results may inform the design of quantum devices based on InAs and InSb semiconductors, e.g., topological qubits utilizing the Majorana zero modes.
We introduce a simple but efficient electronic fitness function (EFF) that describes the electronic aspect of the thermoelectric performance. This EFF finds materials that overcome the inverse relationship between $sigma$ and $S$ based on the complexity of the electronic structures regardless of specific origin (e.g., isosurface corrugation, valley degeneracy, heavy-light bands mixture, valley anisotropy or reduced dimensionality). This function is well suited for application in high throughput screening. We applied this function to 75 different thermoelectric and potential thermoelectric materials including full- and half-Heuslers, binary semiconductors and Zintl phases. We find an efficient screening using this transport function. The EFF identifies known high performance $p$- and $n$-type Zintl phases and half-Heuslers. In addition, we find some previously unstudied phases with superior EFF.
Automated computational materials science frameworks rapidly generate large quantities of materials data useful for accelerated materials design. We have extended the data oriented AFLOW-repository API (Application-Program-Interface, as described in Comput. Mater. Sci. 93, 178 (2014)) to enable programmatic access to search queries. A URI-based search API (Uniform Resource Identifier) is proposed for the construction of complex queries with the intent of allowing the remote creation and retrieval of customized data sets. It is expected that the new language AFLUX, acronym for Automatic Flow of LUX (light), will facilitate the creation of remote search operations on the AFLOW.org set of computational materials science data repositories.
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