50 research outputs found
AFLOW for alloys
Many different types of phases can form within alloys, from highly-ordered
intermetallic compounds, to structurally-ordered but chemically-disordered
solid solutions, and structurally-disordered (i.e. amorphous) metallic glasses.
The different types of phases display very different properties, so predicting
phase formation is important for understanding how materials will behave. Here,
we review how first-principles data from the AFLOW repository and the aflow++
software can be used to predict phase formation in alloys, and describe some
general trends that can be deduced from the data, particularly with respect to
the importance of disorder and entropy in multicomponent systems.Comment: Small AFLOW review submitted to special issue. 6 pages, 4 picture
A RESTful API for exchanging Materials Data in the AFLOWLIB.org consortium
The continued advancement of science depends on shared and reproducible data.
In the field of computational materials science and rational materials design
this entails the construction of large open databases of materials properties.
To this end, an Application Program Interface (API) following REST principles
is introduced for the AFLOWLIB.org materials data repositories consortium.
AUIDs (Aflowlib Unique IDentifier) and AURLs (Aflowlib Uniform Resource
locator) are assigned to the database resources according to a well-defined
protocol described herein, which enables the client to access, through
appropriate queries, the desired data for post-processing. This introduces a
new level of openness into the AFLOWLIB repository, allowing the community to
construct high-level work-flows and tools exploiting its rich data set of
calculated structural, thermodynamic, and electronic properties. Furthermore,
federating these tools would open the door to collaborative investigation of
the data by an unprecedented extended community of users to accelerate the
advancement of computational materials design and development.Comment: 22 pages, 7 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
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
High-entropy high-hardness metal carbides discovered by entropy descriptors
High-entropy materials have attracted considerable interest due to the
combination of useful properties and promising applications. Predicting their
formation remains the major hindrance to the discovery of new systems. Here we
propose a descriptor - entropy forming ability - for addressing
synthesizability from first principles. The formalism, based on the energy
distribution spectrum of randomized calculations, captures the accessibility of
equally-sampled states near the ground state and quantifies configurational
disorder capable of stabilizing high-entropy homogeneous phases. The
methodology is applied to disordered refractory 5-metal carbides - promising
candidates for high-hardness applications. The descriptor correctly predicts
the ease with which compositions can be experimentally synthesized as rock-salt
high-entropy homogeneous phases, validating the ansatz, and in some cases,
going beyond intuition. Several of these materials exhibit hardness up to 50%
higher than rule of mixtures estimations. The entropy descriptor method has the
potential to accelerate the search for high-entropy systems by rationally
combining first principles with experimental synthesis and characterization.Comment: 12 pages, 2 figure