Searching for materials with high refractive index and wide band gap: A first-principles high-throughput study

Naccarato, Francesco; Ricci, Francesco; Suntivich, Jin; Hautier, Geoffroy; Wirtz, Ludger; Rignanese, Gian‐Marco

doi:10.1103/physrevmaterials.3.044602

Cited by 52 publications

(53 citation statements)

References 54 publications

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“…The largest ε el value at a given band gap tends to exhibit an inverse correlation with the band gap, as shown in Fig. 4(a), which is consistent with the previously reported tendency [18,19,22]. This correlation can be understood from Eq.…”

Section: A Validation Of the Dfpt Calculationssupporting

confidence: 90%

“…The third factor is the band gap and widths of the valence and conduction bands because the frequency is the denominator of the integrand in Eq. (2) [22].…”

Section: B Theoretical Background Of Dielectric Constant Calculationmentioning

confidence: 99%

“…1] correspond to the direct band gaps, except for the cases where electronic transitions over the direct gaps are symmetrically forbidden [16,60]. Therefore, the direct gaps should be more relevant to the electronic dielectric constants, particularly for materials with indirect-type band structures, as pointed out by Naccarato et al [22]. However, in an analysis of the statistics, using the minimum or direct gaps does not substantially change the correlation between the band gaps and the dielectric constants (see Fig.…”

Section: A Validation Of the Dfpt Calculationsmentioning

confidence: 99%

“…To explore novel metal oxides and other inorganic materials with desirable dielectric properties and extract the overall physical and chemical trends, databases comprising several thousands of materials have been constructed. Examples include reports on both the electronic and ionic contributions to the static dielectric constants of 1762 oxides [17], 869 nonoxides [18], 1056 inorganic compounds [19], 2393 oxides [20], and 1521 semiconducting inorganic crystals [21], and the electronic static dielectric constants of 4040 materials, of which 3375 are oxides [22].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations

Takahashi

Kumagai

Miyamoto

et al. 2020

Phys. Rev. Materials

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Prediction models of both the electronic and ionic contributions to the static dielectric constants have been constructed using data from density functional perturbation theory calculations of approximately 1200 metal oxides via supervised machine learning. We developed two types of random forest regression models for oxides with the ground-state crystal structures: one model requires only compositional information and the other model also uses structural information. Although the training data included various atomic frameworks, the prediction models performed well even when only compositional information was used as feature descriptors. In prediction of the electronic contributions to the dielectric constants, the accuracies of the regression models with and without structural information were comparable, while the structural descriptors more clearly improved the prediction accuracy for the ionic contributions. We also analyzed the feature importance for prediction of the dielectric constants. The mean atomic mass and mass density were determined to be significant features in prediction of the electronic contributions without and with structural information, respectively. The standard deviation of the principal quantum number and mean neighbor distance variation were found to be important for the respective prediction models of the ionic contributions. The correlations between the dielectric constants and these features are discussed, along with the underlying physical mechanisms.

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Section: A Validation Of the Dfpt Calculationssupporting

confidence: 90%

“…The third factor is the band gap and widths of the valence and conduction bands because the frequency is the denominator of the integrand in Eq. (2) [22].…”

Section: B Theoretical Background Of Dielectric Constant Calculationmentioning

confidence: 99%

Section: A Validation Of the Dfpt Calculationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations

Takahashi

Kumagai

Miyamoto

et al. 2020

Phys. Rev. Materials

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show abstract

“…196 Only non-centrosymmetric materials are SHG-active, and the calculation of the d eff is possible in ABINIT through the 2n + 1 theorem. 9,66 By selecting the non-centrosymmetric materials from databases 197,198 in which the optical dielectric tensors were already available, we built up a new set of candidates including more than 800 compounds aiming at calculating their second-order susceptibility. At this stage, we have computed the nonlinear properties for more than 400 materials employing the above-mentioned ABIFLOWS workflow.…”

Section: Dfpt Shgmentioning

confidence: 99%

ABINIT: Overview and focus on selected capabilities

Romero

Allan

Amadon

et al. 2020

The Journal of Chemical Physics

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223

143

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Computational Discovery and Experimental Demonstration of Boron Phosphide Ultraviolet Nanoresonators

Svendsen

Sugimoto

Assadillayev

et al. 2022

Advanced Optical Materials

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resonances have been realized at visible and infrared wavelengths thanks to the mature lithographic processing of suitable materials, [4] such as silicon (Si), [5] gallium phosphide (GaP), [6] and titanium dioxide (TiO 2 ). [7] It would be desirable to extend the operation of these materials to the ultraviolet, but their small direct band gap energies (≲3 eV) lead to significant absorption losses in the ultraviolet. Wide band gap materials, such as niobium pentoxide [8] and hafnium oxide, [9] offer transparency in the ultraviolet but at the cost of a moderate refractive index (n ≈ 2.1−2.3). Diamond has been theoretically suggested as a potential material, [10,11] but comes with significant nanofabrication challenges. [12] The scarcity of available high-index materials with wide band gap energies calls for the identification of new materials which can advance the rich optical properties of Mie resonances observed in the visible to the ultraviolet. Concurrent advances in first-principles methodology and computing power have recently made it possible to design and discover new materials via high-throughput computations. [13][14][15][16][17] The approach has been successfully applied in several domains, including photovoltaics, transparent conductors, and photocatalysis. [18][19][20] However, to the best of our knowledge, computational discovery of new high-index materials remains largely unexplored. Relevant previous work in this direction has been limited to the static response regime [21,22] reflecting the fact that the major materials databases so far has focused on ground state properties.Here we use high-throughput linear response density functional theory (DFT) to screen an initial set of 2743 elementary and binary materials with the aim to identify isotropic highindex, low loss, and broad band optical materials. For the most promising materials, the computed frequency-dependent complex refractive indices are used as input for Mie scattering calculations to evaluate their optical performance. In addition to the already known high-index materials we identify several new compounds. In particular, boron phosphide (BP) offers a refractive index above three with very low absorption losses in a spectral range spanning from the infrared to the ultraviolet. We then prepare BP nanoparticles and show, by means of darkfield optical measurements and electron energy-loss spectroscopy, that they support size-dependent Mie resonances in the visible and ultraviolet. Finally, we demonstrate a laser reshaping Controlling ultraviolet light at the nanoscale using optical Mie resonances holds great promise for a diverse set of applications, such as lithography, sterilization, and biospectroscopy. Access to the ultraviolet requires materials with a high refractive index and wide band gap energy. Here, the authors systematically search for such materials by computing the frequency-dependent optical permittivity of 338 binary semiconductors and insulators from first principles, and evaluate their scattering properties using Mie theor...

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Searching for materials with high refractive index and wide band gap: A first-principles high-throughput study

Cited by 52 publications

References 54 publications

Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations

Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations

ABINIT: Overview and focus on selected capabilities

Computational Discovery and Experimental Demonstration of Boron Phosphide Ultraviolet Nanoresonators

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