Systematic exploration of the mechanical properties of 13 621 inorganic compounds

Chibani, Siwar; Coudert, François‐Xavier

doi:10.1039/c9sc01682a

Cited by 39 publications

(44 citation statements)

References 58 publications

(76 reference statements)

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“…The zero-temperature elastic and mechanical properties of several 2D-based materials, including their het-erostructures and the less studied trichalcogenides, have been computed (see Table 1 in the Supplementary Material). 11 The predicted elastic and mechanical properties are in excellent agreement with existing data in literature 5,8,[12][13][14][15] . For example, our predicted Y 2D ∼135.86 N/m is in good agreement with recent experiments, which span from 129 to 185 N/m 14,[16][17][18] and previous first-principles calculations that reported values 122 -146 N/m.…”

Section: Introductionsupporting

confidence: 79%

“…5,6 Moreover, the accurate knowledge of the full elastic tensor enables the determination of many other important elastic, mechanical, and thermodynamic properties of materials that are essential for screening, design, and discovery of new materials. 7,8 Recently, and specifically for 2D-based materials (Fig. 1), high-throughput elastic properties of several materials have been calculated and collected in some materials' databases such as the Materials Project, the JARVIS-DFT, and the Computational 2D Materials Database.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Prediction of Temperature-dependent Elastic and Mechanical Properties of 2D Materials

Ekuma¹,

Liu²,

Kastuar³

2021

Preprint

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An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures, including their temperature-dependent mechanical properties and developed a machine learning algorithm for exploring predicted properties.

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Section: Introductionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Efficient Prediction of Temperature-dependent Elastic and Mechanical Properties of 2D Materials

Ekuma¹,

Liu²,

Kastuar³

2021

Preprint

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“…It is common for databases to contain over 10% of erroneous data. Indeed, one study found that 14% of the data describing the elastic properties of crystals in the Materials Project is unphysical 15 . Cleaning steps include removing duplicates, entries with missing values, incoherent or unphysical values, or data type conversions.…”

Section: Data Cleaning and Curationmentioning

confidence: 99%

Best practices in machine learning for chemistry

et al. 2021

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“…10 In the area of negative linear compressibility (NLC), relatively few works have discussed its possible occurrence in zeolitic materials. 24 However, a recent systematic study of inorganic materials has shown that it is more frequent than usually estimated, 25 and it is not as rare as a property as negative Poisson's ratio, or auxeticity. 26 Auxeticity was known to occur in some zeolites since the seminal works of Grima 27 and Evans.…”

Section: Introductionmentioning

confidence: 99%

Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning

2020

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The characterization of the mechanical properties of crystalline materials is nowadays considered a routine computational task in DFT calculations. However, its high computational cost still prevents it from being used in high-throughput screening methodologies, where a cheaper estimate of the elastic properties of a material is required. In this work, we have investigated the accuracy of force field calculations for the prediction of mechanical properties, and in particular for the characterization of the directional Poisson's ratio. We analyze the behavior of about 600,000 hypothetical zeolitic structures at the classical level (a scale three orders of magnitude larger than previous studies), to highlight generic trends between mechanical properties and energetic stability. By comparing these results with DFT calculations on 991 zeolitic frameworks, we highlight the limitations of force field predictions, in particular for predicting auxeticity. We then used this reference DFT data as a training set for a machine learning algorithm, showing that it offers a way to build fast and reliable predictive models for anisotropic properties. The accuracies obtained are, in particular, much better than the current "cheap" approach for screening, which is the use of force 1 fields. These results are a significant improvement over the previous work, due to the more difficult nature of the properties studied, namely the anisotropic elastic response. It is also the first time such a large training data set is used for zeolitic materials.

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Systematic exploration of the mechanical properties of 13 621 inorganic compounds

Abstract: Exploring elastic properties of 13 621 crystals highlights how rare auxeticity and negative linear compressibility are.

Cited by 39 publications

References 58 publications

Efficient Prediction of Temperature-dependent Elastic and Mechanical Properties of 2D Materials

Efficient Prediction of Temperature-dependent Elastic and Mechanical Properties of 2D Materials

Best practices in machine learning for chemistry

Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning

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