The AFLOW Library of Crystallographic Prototypes: Part 1

Mehl, Michael J.; Hicks, David; Toher, Cormac; Levy, Ohad; Hanson, Robert M.; Hart, Gus L. W.; Curtarolo, Stefano

doi:10.1016/j.commatsci.2017.01.017

Cited by 198 publications

(172 citation statements)

References 22 publications

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“…The list of materials includes semiconductors and insulators that belong to different structural prototypes, such as diamond ( A_cF8_227_a 26 ), rocksalt (AB_c-F8_225_a_b 26 ), and fluorite (AB2_cF12_225_a_c 26 ). To maximize the heterogeneity of the data set, materials are selected containing as many different elements as possible from the s-blocks, pblocks, and d-blocks of the periodic table.…”

Section: Validation With Experimentsmentioning

confidence: 99%

“…The task is performed by the internal AFLOW point-factor-space group calculator. 26 Recently, it has also been proposed to obtain the IFCs by inverting the results of many entangled calculations with the use of compressive sensing. 27 Further studies need to be carried out to address the scaling of the algorithm with respect to cut-offs and accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The RMSrD will measure the quantitative difference between AAPL and experimental results: (http://aflow.org/CrystalDatabase/ AB_hP4_186_b_b.html. )), ZrO 2 (P4/nmc, #137, tP6) and corundum (R3c, #167, hR10, D5 1 , A2B3_hR10_167_c_e 26 (http://aflow.org/ CrystalDatabase/A2B3_hR10_167_c_e.html.)) for which there are abundant available experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…Effective use of crystal symmetry can help the process. 73 AAPL uses point, factor, and space group symmetry operations computed by the AFLOW symmetry engine 26 to identify equivalence between single, pairs, and triplets of atoms (positions) and test equivalence between other field quantities, such as differential Φ or finite difference forces ψ (covariantly transforming). To avoid confusion, here indices as superscripts αβγÁÁÁ f g or subscripts αβγÁÁÁ f g are used to identify the character of the symmetry transformation to be applied.…”

Section: Introductionmentioning

confidence: 99%

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An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library

et al. 2017

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One of the most accurate approaches for calculating lattice thermal conductivity, κ ' , is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path: high computational costs and lack of automation in the frameworks using this methodology, which affect the discovery rate of novel materials with ad-hoc properties. Here, the Automatic Anharmonic Phonon Library (AAPL) is presented. It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis, it solves the Boltzmann transport equation to obtain κ ' , and allows a fully integrated operation with minimum user intervention, a rational addition to the current high-throughput accelerated materials development framework AFLOW. An "experiment vs. theory" study of the approach is shown, comparing accuracy and speed with respect to other available packages, and for materials characterized by strong electron localization and correlation. Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

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Section: Validation With Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library

et al. 2017

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“…Before applying these constraints, an understanding of how the constraints map onto the target systems (e.g., the number of atoms in the unit cell, space group, or the effects of local distortions on the structure) is necessary. To facilitate setting up such constraints, we rely on the AFLOW Library of Crystallographic Prototypes [36,37] to generate the initial mapping of real space onto a reduced parameter space that fully describes a system. One can then manually alter the initial mapping to add or lift constraints as needed.…”

Section: Introductionmentioning

confidence: 99%

Parametrically constrained geometry relaxations for high-throughput materials science

et al. 2019

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Reducing parameter spaces via exploiting symmetries has greatly accelerated and increased the quality of electronic-structure calculations. Unfortunately, many of the traditional methods fail when the global crystal symmetry is broken, even when the distortion is only a slight perturbation (e.g. Jahn-Teller like distortions). Here we introduce a flexible and generalizable parametric relaxation scheme, and implement it in the all-electron code FHI-aims. This approach utilizes parametric constraints to maintain symmetry at any level. After demonstrating the method's ability to relax metastable structures, we highlight its adaptability and performance over a test set of 359 materials, across thirteen lattice prototypes. Finally we show how these constraints can reduce the number of steps needed to relax local lattice distortions by an order of magnitude. The flexibility of these constraints enables a significant acceleration of the high-throughput searches for novel materials for numerous applications.

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Automated Computation of Materials Properties

Toher

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Curtarolo

2019

Materials Informatics

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Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated ab-initio frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large datasets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large datasets composed of ab-initio calculations, and the tools that render them programmatically accessible for use in statistical learning applications, are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed. * stefano@duke.edu 1 S. Curtarolo, G. L. W. Hart, M. Buongiorno Nardelli, N. Mingo, S. Sanvito, and O. Levy, The high-throughput highway to computational materials design, Nat. Mater. 12, 191-201 (2013).

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The AFLOW Library of Crystallographic Prototypes: Part 1

Cited by 198 publications

References 22 publications

An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library

An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library

Parametrically constrained geometry relaxations for high-throughput materials science

Automated Computation of Materials Properties

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