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

Plata, José J.; Nath, Pinku; Usanmaz, Demet; Carrete, Jesús; Toher, Cormac; Jong, M. de; Asta, Mark; Fornari, Marco; Nardelli, Marco Buongiorno; Curtarolo, Stefano

doi:10.1038/s41524-017-0046-7

Cited by 80 publications

(73 citation statements)

References 111 publications

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“…The lattice thermal conductivity data were compiled from several resources. 47,49,51,[53][54][55][56][57] The dataset was cleaned in the following procedure. For the materials studied in ref.…”

Section: Data Preparationmentioning

confidence: 99%

A strategy to apply machine learning to small datasets in materials science

2018

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There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision-DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset.

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Section: Data Preparationmentioning

confidence: 99%

A strategy to apply machine learning to small datasets in materials science

2018

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“…Nevertheless, other representations, such as the AFLOW standard primitive representation [4], are often preferred for reducing the computational cost of subsequent calculations/analyses, such as density functional theory calculations [4]. While conversions are always possible, as was done to find the standard conventional cell, in practice it introduces errors in the structural parameters, becoming particularly problematic in "confusion" tolerance regions ( Figure 2) and tolerance-sensitive algorithms, e.g., calculations of force constants [9,42]. To mitigate the need for error-accumulating conversions, a general-representation symmetry algorithm is also incorporated in AFLOW-SYM.…”

Section: E Input Orientation Symmetry Algorithmmentioning

confidence: 99%

“…AFLOW-SYM offers the symmetry operations in the rotation matrix (cartesian and fractional), axis-angle, generator, and quaternion representations [49,50]; while Spglib only provides the rotation matrix representation in its fractional form. AFLOW-SYM also presents the corresponding mappings for each symmetry operation, almost entirely eliminating the need to reapply the operators for symmetry-reduced analyses such as calculating the force constants [9,42]. Along with the factor group coset representatives, AFLOW-SYM provides the lattice point group, reciprocal lattice point group, crystal point group, dual of the crystal point group, site point group, and space group symmetry operators.…”

Section: B Symmetry Characterizations and Representationsmentioning

confidence: 99%

“…The origin of the fixed-point operations differentiates the site symmetry from the lattice/crystal point group, which are centered on the unit cell origin. In the finite difference method for calculating phonons, the unique distortions for a given atomic site are resolved with its site symmetry [9,42]. In AFLOW-SYM, the site symmetry elements are designated by agroup ("atomic site group").…”

Section: Site Point Groupmentioning

confidence: 99%

“…Beyond crystal periodicity, symmetry within the unit cell guides materials classification [1], optimizes materials properties calculations, and instructs structure enumeration methods [2,3]. Careful exploitation of crystal symmetry has made possible the characterization of electronic [4], mechanical [5,6], and thermal properties [7][8][9] in high-throughput fashion [10] -giving rise to large materials properties databases such as AFLOW [1,4,[11][12][13][14][15][16][17][18][19], NoMaD [20], Materials Project [21], and OQMD [22]. As these databases incorporate more properties and grow increasingly integrated, access to rapid and consistent symmetry characterizations becomes of paramount importance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals

Hicks

Oses

Gossett

et al. 2018

Acta Crystallogr A Found Adv

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Determination of the symmetry profile of structures is a persistent challenge in materials science. Results often vary amongst standard packages, hindering autonomous materials development by requiring continuous user attention and educated guesses. This article presents a robust procedure for evaluating the complete suite of symmetry properties, featuring various representations for the point, factor and space groups, site symmetries and Wyckoff positions. The protocol determines a system-specific mapping tolerance that yields symmetry operations entirely commensurate with fundamental crystallographic principles. The self-consistent tolerance characterizes the effective spatial resolution of the reported atomic positions. The approach is compared with the most used programs and is successfully validated against the space-group information provided for over 54 000 entries in the Inorganic Crystal Structure Database (ICSD). Subsequently, a complete symmetry analysis is applied to all 1.7+ million entries of the AFLOW data repository. The AFLOW-SYM package has been implemented in, and made available for, public use through the automated ab initio framework AFLOW.

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Automated Computation of Materials Properties

Toher

Oses

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

Cited by 80 publications

References 111 publications

A strategy to apply machine learning to small datasets in materials science

A strategy to apply machine learning to small datasets in materials science

AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals

Automated Computation of Materials Properties

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