The AFLOW standard for high-throughput materials science calculations

By Camilo E. Calderon, J. Plata, Cormac Toher, Corey Oses, Ohad Levy, Marco Fornari, Amir Natan, Michael J. Mehl, Gus L. W. Hart, Marco Buongiorno Nardelli and 1 more. 2015

356 citations recorded by the source corpus.

The Automatic-Flow (AFLOW) standard for the high-throughput construction of materials science electronic structure databases is described. Electronic structure calculations of solid state materials depend on a large number of parameters which must be understood by researchers, and must be reported by originators to ensure reproducibility and enable collaborative database expansion. We therefore describe standard parameter values for k-point grid density, basis set plane wave kinetic energy cut-off, exchange–correlation functionals, pseudopotentials, DFT+U parameters, and convergence criteria used in AFLOW calculations.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/933709e7125ebbda13443148ab912ea786c3fe8d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. P. Hopkins
2. Jian Luo
3. D. Brenner
4. Stefano Curtarolo
5. Cormac Toher
6. Yoav Lederer
7. Kenneth S. Vecchio

In Materials Chemistry

1. Cormac Toher
2. Corey Oses
3. Kenneth S. Vecchio
4. P. Hopkins
5. Jian Luo
6. D. Brenner
7. Stefano Curtarolo
8. A. Kusne

Citations in the corpus

• Phase stability and mechanical properties of novel high entropy transition metal carbides (2019, methodology): “The energy distributions of these sets of configurations are calculated using density functional theory as implemented in the VASP package [35], with the Perdew, Burke, Ernzerhof (PBE) parameterization of the generalized gradient approximations (GGA) exchange-correlation functional [36], and the AFL”
• Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning (2020, background): “equation (15), equation (16), and two types of angular functions, Gi4,z1,z2and Gi5,z1,z2, in equation (17) and equation (18), respectively.”
• Machine learning modeling of superconducting critical temperature (2017, methodology): “AFLOW is a high-throughput ab initio framework that manages density functional theory (DFT) calculations in accordance with the AFLOW Standard.(21) The Standard ensures that the calculations and derived properties are empirical (reproducible), reasonably well-converged, and above all, consistent (fi”
• The search for high entropy alloys: A high-throughput ab-initio approach (2017, methodology): “All ab-initio energies are obtained using the VASP software [65] within the AFLOW high-throughput framework [29–36] and using the standardized set of parameters [39].”