84 research outputs found
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recently by Kopelevich and Esquinazi (unpublished)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
Challenges and opportunities in linking carbon sequestration, livelihoods and ecosystem service provision in drylands
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
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