Automates the entire workflow of first-principles materials calculations, from structure input to final property extraction, with minimal user intervention.
A vast, curated repository of calculated properties for hundreds of thousands of inorganic crystal structures, accessible via a web interface and REST API.
Defines a consistent format for representing crystal structures, calculation parameters, and material properties across the ecosystem.
Includes modules for calculating a wide range of properties beyond basic energy, such as electronic band structures, elastic tensors, thermodynamic potentials, and phase stability.
Provides an API endpoint to query the AFLOW database programmatically, allowing integration into scripts, data analysis workflows, and external applications.
A researcher aims to find new materials that efficiently convert heat to electricity. They use AFLOW to perform high-throughput screening of thousands of candidate compounds from the database, filtering for those with high Seebeck coefficient and low thermal conductivity predictions. The automated calculations of electronic structure and lattice dynamics enable rapid identification of promising leads, drastically reducing the time from concept to candidate selection for experimental synthesis.
An engineer needs to design a robust, high-entropy alloy for extreme environments. They utilize AFLOW's phase diagram and stability calculation tools to assess the thermodynamic stability of multi-component solid solutions. By calculating mixing enthalpies and simulating phase equilibria across composition space, they can predict which alloy formulations are likely to form stable single-phase structures, guiding experimental alloy development.
A professor teaching computational materials science uses the AFLOW web portal as a teaching tool. Students are assigned to retrieve specific material properties, compare calculated data for different crystal structures, and understand trends across the periodic table. The public, well-documented database serves as a hands-on introduction to materials informatics and the results of quantum mechanical calculations.
A data scientist building a machine learning model to predict material hardness requires a large, clean dataset. They leverage the AFLOW REST API to programmatically download thousands of records containing calculated elastic constants and structural descriptors. The standardized, high-quality data from AFLOW provides an ideal training set, improving model accuracy and reliability compared to using scattered, heterogeneous data sources.
A chemist who has synthesized a new material and determined its approximate crystal structure uses AFLOW to validate its stability. They input the proposed structure into the AFLOW calculation pipeline to compute its formation energy and check it against the convex hull of known phases. This computational validation helps confirm whether the synthesized structure is thermodynamically plausible or might be a meta-stable phase.
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