AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes

Hicks, David; Toher, Cormac; Ford, Denise C.; Rose, Frisco; Santo, Carlo De; Levy, Ohad; Mehl, Michael J.; Curtarolo, Stefano

doi:10.1038/s41524-020-00483-4

Cited by 41 publications

(45 citation statements)

References 48 publications

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“…We integrated all the above three function modules into SPGI. It is different from other similar packages which use the space group information of crystal structures as the predominant criterion for classification 41,42,[61][62][63] . SPGI directly identifies the structure prototypes by learning the local atomic environments of all the crystal structures.…”

Section: Resultsmentioning

confidence: 99%

Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments

et al. 2022

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Recognition of structure prototypes from tremendous known inorganic crystal structures has been an important subject beneficial for material science research and new materials design. The existing databases of inorganic crystal structure prototypes were mostly constructed by classifying materials in terms of the crystallographic space group information. Herein, we employed a distinct strategy to construct the inorganic crystal structure prototype database, relying on the classification of materials in terms of local atomic environments (LAE) accompanied by unsupervised machine learning method. Specifically, we adopted a hierarchical clustering approach onto all experimentally known inorganic crystal structures data to identify structure prototypes. The criterion for hierarchical clustering is the LAE represented by the state-of-the-art structure fingerprints of the improved bond-orientational order parameters and the smooth overlap of atomic positions. This allows us to build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD) containing 15,613 structure prototypes with defined stoichiometries. In addition, we have developed a Structure Prototype Generator Infrastructure (SPGI) package, which is a useful toolkit for structure prototype generation. Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating inorganic materials in a global way as well as accelerating materials discovery process in the datadriven mode.

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Section: Resultsmentioning

confidence: 99%

Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments

et al. 2022

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“…This list was curated from an initial set of over 100 materials, from which we removed all materials that are either thermodynamically unstable or are electrical conductors. This list of materials covers a diverse set of fourteen different binary and ternary crystal structure prototypes [49,50,58].…”

Section: Creating the Datasetmentioning

confidence: 99%

“…All geometries are optimized with symmetry-preserving, parametric constraints until all forces are converged to a numerical precision better than 10 −3 eV/ Å [64]. The constraints are generated using the AFlow XtalFinder Tool [58]. All calculations use the PBEsol functional to calculate the exchangecorrelation energy and an SCF convergence criteria of 10 −6 eV/ Å and 5×10 −4 eV/ Å for the density and forces, respectively.…”

Section: Creating the Datasetmentioning

confidence: 99%

Accelerating Materials-Space Exploration by Mapping Materials Properties via Artificial Intelligence: The Case of the Lattice Thermal Conductivity

Purcell¹,

Scheffler²,

Ghiringhelli³

et al. 2022

Preprint

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Accurate artificial-intelligence models are key to accelerate the discovery of new functional materials with optimal properties for various applications. Examples include superconductivity, catalysis, and thermoelectricity. Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data. For such applications, reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed. Here, we present a general, data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis. We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values. By extracting the most influential material properties from this model, we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials.

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“…There are a plethora of crystal comparison algorithms currently available with a variety of methods ranging from reductions in the dimensionality of input structures into more manageable representations based on intrinsic properties (e.g., periodic point sets, crystallographic information, X-ray powder diffraction, etc.) to transformations of the crystallographic information into a many dimensional configurations (or fingerprint) space (Sadeghi et al, 2013;Valle & Oganov, 2010;Willighagen et al, 2005;Gelder et al, 2001;Karfunkel et al, 1993;Verwer & Leusen, 1998;Mosca & Kurlin, 2020;Thomas et al, 2021;Widdowson et al, 2022;Edelsbrunner et al, 2021;de la Flor et al, 2016;Ferré et al, 2015;Hicks et al, 2021;Gelato & Parthe, 1987;Dzyabchenko, 1994;Lonie & Zurek, 2012;Chuanxun et al, 2017;Ong et al, 2013). These methods can mitigate complexities that arise when dealing with a direct comparison of atomic positions (e.g., atom labeling, special positions, space group conversions, etc.).…”

Section: Introductionmentioning

confidence: 99%

Progressive Alignment of Crystals: Reproducible and Efficient Assessment of Crystal Structure Similarity

Nessler

Okada²,

Hermon

et al. 2022

Preprint

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During in silico crystal structure prediction of organic molecules, millions of candidate structures are often generated. These candidates must be compared to remove duplicates prior to further analysis (e.g., optimization with electronic structure methods) and ultimately compared with structures determined experimentally. The agreement of predicted and experimental structures forms the basis of evaluating the results from the Cambridge Crystallographic Data Centre (CCDC) blind assessment of crystal structure prediction, which further motivates the importance of rigorous alignments. Evaluating crystal structure packings in a reproducible manner requires not only calculating a coordinate root-mean-square deviation (RSMD) for N molecules (or N asymmetric units), but we argue should also include metrics to describe the shape of the compared molecular clusters to account for alternative approaches used to prioritize selection of molecules. Here we describe a flexible algorithm called Progressive Alignment of Crystals (PAC) to evaluate crystal packing similarity using coordinate RMSD and introduce radius of gyration (Rg) as a metric to quantify the shape of the superimposed clusters. We show that the absence of metrics to describe cluster shape adds ambiguity to the results of the CCDC blind assessments because it is not possible to determine whether the superposition algorithm prioritized tightly packed molecular clusters (i.e., to minimize Rg) or prioritized reduced RMSD (i.e., via possibly elongated clusters with relatively larger Rg). For example, we show that when the PAC algorithm described here uses “single linkage” to prioritize molecules for inclusion in the superimposed clusters, our results are nearly identical to those calculated by the widely used program COMPACK. However, we favor the lower Rg values obtained by use of “average linkage” for molecule prioritization because the resulting RMSDs more equally reflect the importance of packing along each dimension. We conclude by showing that the PAC algorithm is faster than COMPACK when using a single process, demonstrate its utility for biomolecular crystals, and finally present parallel scaling up to 64 processes in the open-source code Force Field X.

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AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes

Cited by 41 publications

References 48 publications

Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments

Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments

Accelerating Materials-Space Exploration by Mapping Materials Properties via Artificial Intelligence: The Case of the Lattice Thermal Conductivity

Progressive Alignment of Crystals: Reproducible and Efficient Assessment of Crystal Structure Similarity

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