AFLOW

AFLOW
Name	AFLOW
Developed by	D. Hicks, C. Toher, S. Curtarolo, and collaborators
Initial release	2011
Programming language	C++, Python, Bash
Operating system	Linux, macOS
Repository	Various institutional repositories
License	Open-source variants and proprietary components

AFLOW is a software framework and materials informatics ecosystem designed for high-throughput computational materials discovery and property prediction. It automates density functional theory workflows, organizes computed materials data, and interfaces with repositories and machine learning tools to accelerate research in materials science, condensed matter physics, and chemistry. AFLOW integrates with electronic structure codes, database services, and community standards to enable reproducible, large-scale screening of inorganic crystals, alloys, and compounds.

Overview

AFLOW functions as an automation layer and data aggregator for first-principles calculations performed with electronic structure engines such as VASP, Quantum ESPRESSO, ABINIT, WIEN2k, and GPAW. It orchestrates tasks including structure generation, relaxation, total-energy evaluation, phonon calculations, and defect modeling while tracking provenance compatible with initiatives like the Materials Project, Open Quantum Materials Database, NOMAD, and Citrine Informatics. The framework supports property extraction for phase stability, electronic band structures, density of states, elastic tensors, and thermal conductivities, interfacing with visualization and analysis tools used by researchers affiliated with institutions like Duke University, Massachusetts Institute of Technology, and Drexel University.

History and Development

Development began in the late 2000s within groups led by researchers trained at centers such as Brown University and Carnegie Mellon University, with project milestones announced in conferences including MRS Meeting, APS March Meeting, and Gordon Research Conferences. Early papers compared AFLOW outputs against benchmarks from NIST and collaborations with labs at Oak Ridge National Laboratory and Argonne National Laboratory. Over successive releases the codebase expanded to incorporate standards from organizations like ISO and to interoperate with community projects such as pyiron and ASE while responding to funding and collaboration from agencies including the National Science Foundation and the Department of Energy.

Architecture and Components

The architecture is modular, comprising workflow managers, structure libraries, calculation wrappers, and database exporters. Key components interface with the GNU Compiler Collection toolchain, use build systems akin to CMake, and rely on scripting via Python (programming language), Bash, and C++ modules. Data schema design aligns with ontologies promoted by CODATA and leverages serialization formats accepted by JSON and HDF5 ecosystems. AFLOW utilities include structure prototype libraries, symmetry analyzers comparable to FINDSYM, and post-processing suites analogous to offerings from Wannier90 and BoltzTraP.

Data Repositories and Databases

AFLOW-generated datasets feed into curated repositories that catalog crystal structures, computed formation enthalpies, and derived thermodynamic and electronic properties. These resources are curated alongside datasets from ICSD, Pauling File, Crystallography Open Database, and project-driven collections like the Open Materials Database and the Harvard Clean Energy Project. Metadata practices mirror guidelines from DataCite and facilitate cross-references to entries in institutional archives at universities such as Northwestern University and Brown University as well as national laboratories including Lawrence Berkeley National Laboratory.

Methods and Workflows

Workflows implemented include high-throughput geometry optimization, k-point convergence, Hubbard U scans, phonon dispersion via finite-displacement and perturbation approaches, and vacancy and interstitial defect formation modeling. Automation sequences reflect methodologies published in journals such as Physical Review Letters, Nature Materials, Chemistry of Materials, and Journal of Chemical Physics. AFLOW workflows are parameterized to reproduce standards established in community benchmarks from groups at MIT, Stanford University, and ETH Zurich, and they support integration with machine learning pipelines inspired by work from Google DeepMind, IBM Research, and academic teams at University of California, Berkeley.

Applications and Impact

Researchers use AFLOW datasets and tools to identify thermodynamically stable phases, screen for thermoelectric, photovoltaic, magnetic, and superconducting candidates, and explore high-entropy alloys and low-dimensional materials such as two-dimensional crystals studied in groups at Columbia University and University of Manchester. Outcomes have influenced experimental programs at facilities including Brookhaven National Laboratory, SLAC National Accelerator Laboratory, and synchrotrons like Diamond Light Source and Advanced Photon Source. AFLOW-driven discoveries have been reported in cross-disciplinary collaborations involving corporations in the materials and energy sectors, as well as academic consortia connected to ERC-funded projects and Horizon 2020 initiatives.

Licensing and Availability

AFLOW components are distributed under a mix of open-source licenses and community-sharing agreements; some modules and associated databases are accessible for academic research while enterprise usage may require institutional arrangements. Source code and precomputed datasets have historically been mirrored in institutional repositories and shared via community platforms used by projects like GitHub and Zenodo, with documentation and user support provided through workshops at venues such as TMS Annual Meeting and summer schools at research centers like MatSci Summer Schools.

Category:Computational materials science Category:Materials databases