48 research outputs found
The structure and composition statistics of 6A binary and ternary crystalline materials
The fundamental principles underlying the arrangement of elements into solid
compounds with an enormous variety of crystal structures are still largely
unknown. This study presents a general overview of the structure types
appearing in an important subset of the solid compounds, i.e., binary and
ternary compounds of the 6A column oxides, sulfides and selenides. It contains
an analysis of these compounds, including the prevalence of various structure
types, their symmetry properties, compositions, stoichiometries and unit cell
sizes. It is found that these compound families include preferred
stoichiometries and structure types that may reflect both their specific
chemistry and research bias in the available empirical data. Identification of
non-overlapping gaps and missing stoichiometries in these structure populations
may be used as guidance in the search for new materials.Comment: 19 pages, 13 figure
High throughput thermal conductivity of high temperature solid phases: The case of oxide and fluoride perovskites
Using finite-temperature phonon calculations and machine-learning methods, we
calculate the mechanical stability of about 400 semiconducting oxides and
fluorides with cubic perovskite structures at 0 K, 300 K and 1000 K. We find 92
mechanically stable compounds at high temperatures -- including 36 not
mentioned in the literature so far -- for which we calculate the thermal
conductivity. We demonstrate that the thermal conductivity is generally smaller
in fluorides than in oxides, largely due to a lower ionic charge, and describe
simple structural descriptors that are correlated with its magnitude.
Furthermore, we show that the thermal conductivities of most cubic perovskites
decrease more slowly than the usual behavior. Within this set, we also
screen for materials exhibiting negative thermal expansion. Finally, we
describe a strategy to accelerate the discovery of mechanically stable
compounds at high temperatures.Comment: 9 pages, 6 figure
Machine learning modeling of superconducting critical temperature
Superconductivity has been the focus of enormous research effort since its
discovery more than a century ago. Yet, some features of this unique phenomenon
remain poorly understood; prime among these is the connection between
superconductivity and chemical/structural properties of materials. To bridge
the gap, several machine learning schemes are developed herein to model the
critical temperatures () of the 12,000+ known superconductors
available via the SuperCon database. Materials are first divided into two
classes based on their values, above and below 10 K, and a
classification model predicting this label is trained. The model uses
coarse-grained features based only on the chemical compositions. It shows
strong predictive power, with out-of-sample accuracy of about 92%. Separate
regression models are developed to predict the values of for
cuprate, iron-based, and "low-" compounds. These models also
demonstrate good performance, with learned predictors offering potential
insights into the mechanisms behind superconductivity in different families of
materials. To improve the accuracy and interpretability of these models, new
features are incorporated using materials data from the AFLOW Online
Repositories. Finally, the classification and regression models are combined
into a single integrated pipeline and employed to search the entire Inorganic
Crystallographic Structure Database (ICSD) for potential new superconductors.
We identify more than 30 non-cuprate and non-iron-based oxides as candidate
materials.Comment: 17 pages, 7 figure
AFLOW-SYM: Platform for the complete, automatic and self-consistent symmetry analysis of crystals
Determination of the symmetry profile of structures is a persistent challenge
in materials science. Results often vary amongst standard packages, hindering
autonomous materials development by requiring continuous user attention and
educated guesses. Here, we present a robust procedure for evaluating the
complete suite of symmetry properties, featuring various representations for
the point-, factor-, space groups, site symmetries, and Wyckoff positions. The
protocol determines a system-specific mapping tolerance that yields symmetry
operations entirely commensurate with fundamental crystallographic principles.
The self consistent tolerance characterizes the effective spatial resolution of
the reported atomic positions. The approach is compared with the most used
programs and is successfully validated against the space group information
provided for over 54,000 entries in the Inorganic Crystal Structure Database.
Subsequently, a complete symmetry analysis is applied to all 1.7 million
entries of the AFLOW data repository. The AFLOW-SYM package has been
implemented in, and made available for, public use through the automated,
framework AFLOW.Comment: 24 pages, 6 figure
Universal fragment descriptors for predicting properties of inorganic crystals
Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules