AFLOW: An automatic framework for high-throughput materials discovery

By Stefano Curtarolo, W. Setyawan, Gus L. W. Hart, M. Jahnátek, R. Chepulskii, Richard H. Taylor, Shidong Wang, Junkai Xue, Kesong Yang, Ohad Levy and 4 more. 2012

1,411 citations recorded by the source corpus.

Abstract Recent advances in computational materials science present novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds and metastable structures, electronic structure, surface, and nano-particle properties. The practical realization of these opportunities requires systematic generation and classification of the relevant computational data by high-throughput methods. In this paper we present A flow (Automatic Flow), a software framework for high-throughput calculation of crystal structure properties of alloys, intermetallics and inorganic compounds. The A flow software is available for the scientific community on the website of the materials research consortium, aflowlib.org. Its geometric and electronic structure analysis and manipulation tools are additionally available for online operation at the same website. The combination of automatic methods and user online interfaces provide a powerful tool for efficient quantum computational materials discovery and characterization.

Subfields: Catalysis, Materials Chemistry

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Citations in the corpus

The AFLOW standard for high-throughput materials science calculations (2015, methodology): “For example, sets of RELAX and STATIC calculations for different cell volumes and/or atomic configurations are used to calculate thermal and mechanical properties by the AGL [12] and APL [1] methods inplemented within the AFLOW framework.”
Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling (2014, methodology): “The 79 , 057 half-Heusler systems are calculated with the high-throughput framework AFLOW [4, 25, 47, 48] based on ab-initio calculations of the energies by the VASP software [49] with projector augmented waves (PAW) pseudopotentials [50], and Perdew, Burke and Ernzerhof exchange-correlation…”
The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals (2018, background): “Some of the larger repositories, together containing millions of material entries, are the Materials Project [22], the Automatic Flow for Materials Discovery (AFLOWLIB) [23], the Open Quantum Materials Database (OQMD) [24, 25], and the Novel Materials Discovery (NOMAD) Repository [26].”
Nanograined Half‐Heusler Semiconductors as Advanced Thermoelectrics: An Ab Initio High‐Throughput Statistical Study (2014, methodology): “27,28 This list contains all conceivable unique half-Heusler structures that can be built us-ing elements from the list {Ag, Al, As, Au, B, Ba, Be, Bi, Br, Ca, Cd, Cl, Co, Cr, Cu, Fe, Ga, Ge, Hf, Hg, In, Ir, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, Os, P, Pb, Pd, Pt, Re, Rh, Ru, Sb, Sc, Se, Si, Sn, Sr,…”
Phase stability and mechanical properties of novel high entropy transition metal carbides (2019, methodology): “To calculate the EFA, a set of 49 configurations is generated using the partial occupation (POCC) module [33] of the AFLOW (Automatic FLOW) framework [34].”