Ionic <i>vs.</i> van der Waals layered materials: identification and comparison of elastic anisotropy

McKinney, Robert; Gorai, Prashun; Manna, Sukriti; Toberer, Eric S.; Stevanović, Vladan

doi:10.1039/c8ta04933e

Cited by 27 publications

(49 citation statements)

References 69 publications

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“…Layered materials with such isotropic transport are often where we find ambiguity in classification. 67 In Figs. 7d-f, we show three examples of layered materials with low  L,min which also demonstrate a large amount of anisotropy within  L .…”

Section: Application To Layered Materialsmentioning

confidence: 98%

“…Over layered materials are more tightly bound than vdW layered materials and therefore do not tend to demonstrate large di↵erences between intralayer and interlayer bonding. 67,[81][82][83] Layered materials with low  L For very low values of  L (Fig. 5b), we find that ionic layered materials produce the lowest values, but that they are nearly isotropic (examples shown in Fig.…”

Section: Application To Layered Materialsmentioning

confidence: 99%

“…67 VdW and ionic layered materials can demonstrate a high degree of elastic anisotropy and the layering direction is not necessarily a predictor of the directional variance of elastic properties, leading to diverse and unexpected directional elastic behavior. 67 Since the lattice thermal conductivity stems in large part from elastic properties, it is also reasonable to assume that we will find a number of materials which demonstrate large variability in the thermal transport.…”

Section: Validation Of Anisotropic  L Modelmentioning

confidence: 99%

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Rapid Prediction of Anisotropic Lattice Thermal Conductivity: Application to Layered Materials

et al. 2019

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Thermal conductivity plays a crucial role in many applications; use of single-crystal and textured polycrystalline materials in such applications necessitate understanding the anisotropy in thermal transport. Measurement of anisotropic lattice thermal conductivity  L (✓,) is quite challenging. To address this need through computations, we build upon our previously developed isotropic model for  L and incorporate the directional (angular) dependence by using the elastic tensor obtained from ab initio calculations and the Christo↵el equations for speed of sound. With the anisotropic speed of sound and intrinsic material properties as input parameters, we can predict the direction-dependent  L (✓,). We validate this new model by comparing with experimental data from the literature-predicted  L is within an average factor di↵erence of 1.8 of experimental measurements, spanning 5 orders of magnitude in  L. To demonstrate the utility and computational-tractability of this model, we calculate  L (✓,) of ⇠2200 layered materials that are expected to exhibit anistropic thermal transport. We consider both van der Waals and ionic layered structures with binary and ternary chemistries and analyze the anisotropy in their  L. The large-scale study has revealed many layered structures with interesting anisotropy in  L .

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Section: Application To Layered Materialsmentioning

confidence: 98%

Section: Application To Layered Materialsmentioning

confidence: 99%

Section: Validation Of Anisotropic  L Modelmentioning

confidence: 99%

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Rapid Prediction of Anisotropic Lattice Thermal Conductivity: Application to Layered Materials

et al. 2019

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“…A relatively high tolerance of 10 À 6 eV for energy convergence was employed in these calculations. Three independent elastic constants as shown in Table 1, n atoms � n Kpoints � 1000, were determined for cubic system by employing suitable lattice distortions [45,37,65,38,39,40] represented by a strain tensor, e, (defined by equation 1) in such a way that the new lattice vectors r in the distorted lattice is given by r 0 ¼ ðI þ eÞr where I is the unit matrix.…”

Section: Dft Calculationsmentioning

confidence: 99%

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

et al. 2020

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Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (� 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. Here, we introduce an active learning (AL) scheme that trains a NN model on-thefly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (�1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with �500 total reference calculations. Using an extensive DFT test set of~1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/ Å of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

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“…By identifying structures with interlayer sodium atoms, Zhang et al 19 have investigated promising layered cathode materials for sodium-ion batteries. McKinney et al 20 have extended this search to general 'ionic layered' structures.…”

Section: B Defining the Scoring Parametermentioning

confidence: 99%

Definition of a scoring parameter to identify low-dimensional materials components

Larsen

Pandey

Strange

et al. 2019

Phys. Rev. Materials

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The last decade has seen intense research in materials with reduced dimensionality. The low dimensionality leads to interesting electronic behavior due to electronic confinement and reduced screening. The investigations have to a large extent focused on 2D materials both in their bulk form, as individual layers a few atoms thick, and through stacking of 2D layers into heterostructures. The identification of low-dimensional compounds is therefore of key interest. Here, we perform a geometric analysis of material structures, demonstrating a strong clustering of materials depending on their dimensionalities. Based on the geometric analysis, we propose a simple scoring parameter to identify materials of a particular dimension or of mixed dimensionality. The method identifies spatially connected components of the materials and gives a measure of the degree of "1D-ness," "2D-ness," etc., for each component. The scoring parameter is applied to the Inorganic Crystal Structure Database and the Crystallography Open Database ranking the materials according to their degree of dimensionality. In the case of 2D materials the scoring parameter is seen to clearly separate 2D from non-2D materials and the parameter correlates well with the bonding strength in the layered materials. About 3000 materials are identified as one-dimensional, while more than 9000 are mixed-dimensionality materials containing a molecular (0D) component. The charge states of the components in selected highly ranked materials are investigated using density functional theory and Bader analysis showing that the spatially separated components have either zero charge, corresponding to weak interactions, or integer charge, indicating ionic bonding.

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Ionic vs. van der Waals layered materials: identification and comparison of elastic anisotropy

Cited by 27 publications

References 69 publications

Rapid Prediction of Anisotropic Lattice Thermal Conductivity: Application to Layered Materials

Rapid Prediction of Anisotropic Lattice Thermal Conductivity: Application to Layered Materials

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Definition of a scoring parameter to identify low-dimensional materials components

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