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).Comment: Project homepage: http://gurka.fysik.uu.se/ESP

Evolving properties of two dimensional materials, from graphene to graphite

We have studied theoretically, using density functional theory, several materials properties when going from one C layer in graphene to two and three g raphene layers and on to graphite. The properties we have focused on are the elastic constants, electronic structure (energy bands and density of state s), and the dielectric properties. For any of the properties we have investigated the modification due to an increase in the number of graphene layers is within a few percent. Our results are in agreement with the analysis presented recently by Kopelevich and Esquinazi (unpublished)

Carbon release by selective alloying of transition metal carbides

We have performed first principles density functional theory calculations on TiC alloyed on the Ti sublattice with 3d transition metals ranging from Sc to Zn. The theory is accompanied with experimental investigations, both as regards materials synthesis as well as characterization. Our results show that by dissolving a metal with a weak ability to form carbides, the stability of the alloy is lowered and a driving force for the release of carbon from the carbide is created. During thin film growth of a metal carbide this effect will favor the formation of a nanocomposite with carbide grains in a carbon matrix. The choice of alloying elements as well as their concentrations will affect the relative amount of carbon in the carbide and in the carbon matrix. This can be used to design the structure of nanocomposites and their physical and chemical properties. One example of applications is as low-friction coatings. Of the materials studied, we suggest the late 3d transition metals as the most promising elements for this phenomenon, at least when alloying with TiC.Comment: 9 pages, 6 figure

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure