Predicted Band Gap Research Articles

Electronic structure and optical properties of TeO2 polymorphs

The structural, electronic and, optical properties of TeO2 with three crystal structures are calculated using the density functional theory (DFT). The lattice parameters (a and c), bulk modulus (B) and its pressure derivative (B′) are all calculated for three polymorphs of TeO2: paratellurite (α-TeO2), tellurite (β-TeO2) and a recently synthesized phase (γ-TeO2). These properties at equilibrium phase agree well with the available theoretical and experimental values. The results reveal that the three crystalline polymorphs are wide-gap semiconductors. Moreover, the predicted band gaps for the considered phases by using the semi-local functional of Tran and Blaha (TB-mBJ) approach are qualitatively more accurate than those obtained by the simplest generalized gradient approximation (GGA). Finally, the optical properties of TeO2 have been predicted and discussed.

QTAIM method for accelerated prediction of band gaps in perovskites

Efficient prediction of electronic properties of semiconductors is a cornerstone in the rational design of materials for various technological applications, including optoelectronics, photovoltaics and catalysis. Topological analysis of electron density is a powerful tool to unravel the correlations between the composition of isostructural compounds and their electronic properties. Resorting to this approach in theory or in experiment requires an elaboration of descriptors connecting the composition with the electronic structure characteristics. In the current work, the application of chemical topology for prediction of band gaps is illustrated for the model systems of perovskite compounds. The correlations between the band gaps and the electron density at the bond critical points are established, enabling the construction of composition–property maps. The procedure applying PBE-based descriptors for evaluation of the band gaps, calculated with resources-demanding methods, such as hybrid functionals or GW, is outlined. Finally, it is demonstrated how topological indices can be used to predict the band gaps for yet unsynthesized materials on the basis of available experimental data for isostructural compounds.

Predictions of bandgap and subbands of γ−Al2O3 in presence of intrinsic point defects by DFT+TB-mBJ

The electronic structure of γ-Al2O3 in the presence of the Al and O defects is investigated within the density functional theory using the PBE-GGA and TB-mBJ schemes. The formation of the Al vacancy produces acceptor-like sublevels above the valence band maximum, consistent with the previous G0W0 calculations. Furthermore, the formation of the oxygen vacancy generates donor-like sublevels below the conduction band minimum, which is consistent with experiment. The donor sublevels are predicted more accurately by TB-mBJ than PBE-GGA, as electron charge density is calculated to be larger at the O vacancy site by TB-mBJ within its optimized c-factor.

Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride

It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials. However, predicting such effects is challenging due to the large range of atomic configurations that are possible. Here, we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations. A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks. Bandgaps calculated by ab initio calculations, and corresponding structures, were used as training datasets. The trained networks were then used to predict bandgaps of systems with various configurations. For 4 × 4 and 5 × 5 supercells they accurately predict bandgaps, with a R2 of >90% and root-mean-square error of ~0.1 eV. The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6 × 6 supercells. This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials. Moreover, given the ubiquitous existence of configurations in materials, this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales.

Feature Engineering for Materials Chemistry—Does Size Matter?

The effects of structural featurizers in the prediction of band gaps have been investigated through machine learning by application to a silver nanoparticle data set and 2254 potential light-harvesting materials with known band gaps. Elemental properties were extended with structural features via Voronoi polyhedra, allowing for neighbor effects and thus presumably giving a better representation of the extended system. However, we did not find any noticeably significant difference in the predictive performance of our model. The biggest improvement in our model was due to inclusion of band gaps calculated using density functional theory. This resulted in a model that could predict the band gaps of the 2254 light-harvesting materials in the data set with an accuracy reflected in a root-mean-square error of 0.232 eV and mean absolute error of 0.142 eV. Furthermore, the good performance of our model was transferable to the prediction of a set of 72 experimental band gaps that were independent of the training set, giving a root-mean-square error of 0.91 eV and mean absolute error of 0.76 eV.

Efficient and accurate calculation of band gaps of halide perovskites with the Tran-Blaha modified Becke-Johnson potential

We report on a reoptimization of the Tran-Blaha modified Becke-Johnson (TB-mBJ) potential dedicated to the prediction of the band gaps of 3-dimensional (3D) and layered hybrid organic-inorganic perovskites (HOP) within pseudopotential-based density functional theory methods. These materials hold promise for future photovoltaic and optoelectronic applications. We begin by determining a set of parameters for 3D HOP optimized over a large range of materials. Then we consider the case of layered HOP. We design an empirical relationship that facilitates the prediction of band gaps of layered HOP with arbitrary interlayer molecular spacers with a computational cost considerably lower than more advanced methods like hybrid or GW. Our study also shows that substituting interlayer molecular chains of layered HOP with Cs atoms is an appealing and cost-effective route to band gap calculations. Finally, we discuss on the pitfalls and limitations of TB-mBJ for HOP, notably its tendency to overestimate the effective masses due to the narrowing of the band dispersions. We expect our results to extend the use of TB-mBJ for other low-dimensional materials.

Band gap and band alignment prediction of nitride-based semiconductors using machine learning

Machine learning has been utilized to accurately predict band gap and band alignment of wurtzite nitrides in a large design space.

A computational study of hydrogen doping induced metal-to-insulator transition in CaFeO3, SrFeO3, BaFeO3 and SmMnO3.

The metal-to-insulator transition (MIT) in rare earth perovskite oxides has drawn significant research interest for decades to unveil the underlying physics and develop novel electronic materials. Recently, chemical doping induced MIT in SmNiO3 has been observed experimentally, with its resistivity changed by eight orders of magnitude. The mechanism of switching from one singly occupied Ni eg orbital to two singly occupied eg orbitals upon doping has been proposed by experimentalists and verified by computation. Here, we tested if this mechanism can be generally applied to other perovskite oxides with non-Ni B site elements. We applied first principles density functional theory (DFT) to study a series of perovskite oxides, CaFeO3, SrFeO3, BaFeO3 and SmMnO3. We investigated the geometry and electronic structures of pure and hydrogen doped oxides. We found that pure CaFeO3, SrFeO3 and BaFeO3 are metallic while pure SmMnO3 has a small band gap of 0.69 eV. Upon hydrogen doping, band gap opening was predicted for all four oxides: HSE06 predicted band gap values of 1.58 eV, 1.40 eV, 1.20 eV and 2.55 eV for H-doped CaFeO3, SrFeO3, BaFeO3 and SmMnO3, respectively. This finding opens up research opportunities for exploring a broader range of materials for MIT to be used in optical and electronic devices.

Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles.

Antibacterial activities and cytotoxicity of metal oxide nanoparticles are determined by their special band structures, which also influence their potential ecological risks. Traditional experimental determination of the band gap is time-consuming, while the accuracy of theoretical computation depends on the selected algorithm, for which higher precision algorithms, being more expensive, can give a more accurate band gap. Therefore, in this study, a quantitative structure–property relationship (QSPR) model, highlighting the influence of crystalline type and material size, was developed to predict the band gap of metal oxide nanoparticles rapidly and accurately. The structural descriptors for metal oxide nanoparticles were generated via quantum chemistry computations, among which heat of formation and beta angle of the unit cell were the most important parameters influencing band gaps. The developed model shows great robustness and predictive ability (R2 = 0.848, RMSE = 0.378 eV, RMSECV = 0.478 eV, QEXT2 = 0.814, RMSEP = 0.408 eV). The current study can assist in screening the ecological risks of existing metal oxide nanoparticles and may act as a reference for newly designed materials.

Construction of Band Gap Prediction Model for π-conjugated Polymers Using Aromatic/Quinoid and Donor/Acceptor Properties

Construction of Band Gap Prediction Model for π-conjugated Polymers Using Aromatic/Quinoid and Donor/Acceptor Properties

Conference report: 2018 materials and data science hackathon (MATDAT18)

MATDAT18 organizers and participants.

Understanding the behavior of electronic and phonon transports in germanium based two dimensional chalcogenides

Electronic and phonon transport properties of buckled GeTe and GeSe monolayers were investigated by combining density functional theory with lattice dynamics approach. For accurate prediction of electronic bandgaps, the PBE0 hybrid functional was employed, and the bandgap values were found to be 2.33 eV and 3.55 eV for GeTe and GeSe monolayers, respectively. Electronic transport coefficients were calculated using Boltzmann transport equations implemented in the BOLTZTRAP code. The Seebeck coefficients of GeTe (2680.94 μV/K) and GeSe (2981.81 μV/K) monolayers were found to be quite higher than those of their other allotropes. The out of plane ZA mode exhibits a quadratic nature near the Γ point of the Brillouin zone, which is attributed to the flexural phonon modes in both GeTe and GeSe monolayers. Strong anharmonicity found in the GeTe monolayer compared to the GeSe monolayer leads to lower lattice thermal conductivity in the GeTe monolayer. The room temperature lattice thermal conductivity of both monolayers was found to be quite low. A comprehensive analysis of group velocity for all phonon modes shows that the ZA mode contributes less to the lattice thermal conductivity of the GeTe monolayer than to that of the GeSe monolayer. An analysis of three-phonon scattering reveals that more scattering channels are available for phonon scattering in GeTe, which leads to lower thermal conductivity in the GeTe monolayer. The GeSe monolayer has a larger figure of merit than the GeTe monolayer, but it may have low output power because of its low electrical conductivity.

New materials band gap prediction based on the high-throughput calculation and the machine learning

The bandgap often plays an important role in functional materials applications. For example, optoelectronic materials are generally wide bandgap semiconductors, while thermoelectric materials are narrow bandgap semiconductor materials. Therefore, predicting the bandgap rapidly and accurately for a given class of materials structures has great scientific importance for the functional materials applications. However, considering that the method of obtaining high-precision band gaps based on first-principles high-throughput calculations is time consuming and inefficient, and it is also not realistic to systematically measure a large number of material system band gaps. Machine learning methods based the statistics may be a promising alternative. This paper designs an ensemble learning model for effectively and accurately predicting bandgap values. Based on the calculated band gap values of diamond-like structures in thermoelectric materials, on the one hand, single component substitution strategy was used to generate large quantities of similar compounds, and the repetitive structures was filtered out by using the structural repeatability examination technique, resulting in 356 unique material structures. On the other hand, in combination with machine learning techniques, an efficient band gap prediction model was constructed, and by which the band gap values ​​of 50 similar material systems are predicted and verified. As is the result of the experiment, this prediction model has 77.73% accuracy. It is enough robustness and stability to be widely used in thermoelectric materials application scenarios which require large band gap prediction.

Predicting accurate cathode properties of layered oxide materials using the SCAN meta-GGA density functional

Layered lithium intercalating transition metal oxides are promising cathode materials for Li-ion batteries. Here, we scrutinize the recently developed strongly constrained and appropriately normed (SCAN) density functional method to study structural, magnetic, and electrochemical properties of prototype cathode materials LiNiO2, LiCoO2, and LiMnO2 at different Li-intercalation limits. We show that SCAN outperforms earlier popular functional combinations, providing results in considerably better agreement with experiment without the use of Hubbard parameters, and dispersion corrections are found to have a small effect. In particular, SCAN fares better than Perdew–Burke–Ernzerhof (PBE) functional for the prediction of band-gaps and absolute voltages, better than PBE+U for the electronic density of states and voltage profiles, and better than both PBE and PBE+U for electron densities and in operando lattice parameters. This overall better performance of SCAN may be ascribed to improved treatment of localized states and a better description of short-range dispersion interactions.

DFT prediction of band gap in organic-inorganic metal halide perovskites: An exchange-correlation functional benchmark study

We report a comprehensive and systematic study of two halide perovskites by using density functional theory calculations. Two structurally different perovskites have been studied, which are cubic CH3NH3PbI3 (MAPI) and HC(NH2)2PbI3 (FAPI). We have used twenty-four exchange-correlation functionals, ranging from three LDA functionals, ten GGA functionals, seven MGGA and four hybrids among others have been tested, in order to determine the accuracy of these methods for the prediction of band gaps. Moreover, we have studied several possibilities to tackle the calculations of perovskites. That is, we have tested Numerical Atomic Orbitals with All Electron Relativistic calculations and compared to Plane Wave framework with pseudopotentials and relativistic corrections. Moreover, we have studied different k-points set, pseudopotentials types, with and without cell optimizations, with and without dispersion corrections and with and without Spin Orbit coupling. The results show that PBE and RPBE exhibit a good compromise between the accuracy of the results and computational demands.

Flexural wave band gaps in metamaterial plates: A numerical and experimental study from infinite to finite models

The proposed numerical design approach investigates both infinite and finite models and includes an experimental validation of elastic wave absorption and vibration suppression for flexural wave band gaps in metamaterial plates. The resonant metamaterial is obtained using a 2-dimensional periodic array of resonators (mass-screws) mounted to a thin homogeneous plate. The sensitivity analysis of the band gap frequency range takes into account the uncertainties of all the design parameters of the metamaterial plate. The theoretical approach uses the finite element method (FEM) to compare the predicted band gaps, which are derived from infinite and finite models of the metamaterial. An automatic method is proposed to detect the frequency ranges of band gaps in the finite metamaterial based on the behavior of the corresponding bare plate. Directional plane wave excitation and point force excitation are applied to evaluate the efficiency of the detection method. The results of these analyses are compared with experimental measurements. The frequency ranges of experimental vibration attenuation are in good agreement with the theoretically predicted complete and directional band gaps.

Many-body perturbation theory study of type-II InAs/GaSb superlattices within the GW approximation

Recent studies suggest that the many-body perturbation theory in the partially self-consistent GW (GW0) approximation significantly improves the prediction of band gaps in various semiconductors. In this work, we employed GW formalism to study the electronic structure of type-II InAs/GaSb strained-layer superlattices (T2SLs). T2SLs considered in this study, denoted by (monolayers of InAs, monolayers of GaSb) are (), (), (), (), and (). The InSb-type interfacial layer was introduced in the structures to resemble the actual growth condition in our laboratories. The electronic band gaps are indirect in all the structures. The band gaps at the center of the Brillouin zone show good agreement with experimental data. This study is the first step to investigate the electronic, optical, and defect characteristics of T2SLs within a parameter-free ab initio method.

Wave propagation in acoustic metamaterials with resonantly shunted cross-shape piezos

Cross-shape piezoelectric patches were originally proposed to improve the band-gap properties of acoustic metamaterials with shunting circuits. The dispersion curves are characterized through the application of finite element method. Also, the theoretical band-gap predictions are verified by simulation results obtained from COMSOL. The investigation results show that the proposed scheme distinguishes itself from the conventional square patches by broader band gaps, whose bandwidth is almost doubled. The inherent capacitance of the piezoelectric patch is strongly related to the boundary conditions, so the local resonant band gap is strongly affected by the shape of piezoelectric patches as well. As a result, the band-gap width and location of metamaterials with different shape patches are rather different, even with the same size patches. Also, negative modulus (NM) and Poisson’s ratio were observed around the resonant frequencies. The transmission properties of finite periods agree well with band-gap predictions. An obvious attenuation zone (AZ) is produced around the band-gap location, in which the wave propagation is decayed strongly. Similarly, the width of AZ of the proposed metamaterial is much larger than that of the conventional one. Hence, the proposed scheme demonstrates more advantages in the application to vibration isolation when compared with the conventional.

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

MXenes are two-dimensional (2D) transition metal carbides and nitrides, and are invariably metallic in pristine form. While spontaneous passivation of their reactive bare surfaces lends unprecedented functionalities, consequently a many-folds increase in number of possible functionalized MXene makes their characterization difficult. Here, we study the electronic properties of this vast class of materials by accurately estimating the band gaps using statistical learning. Using easily available properties of the MXene, namely, boiling and melting points, atomic radii, phases, bond lengths, etc., as input features, models were developed using kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms. Among these, the GPR model predicts the band gap with lowest root-mean-squared error (rmse) of 0.14 eV, within seconds. Most importantly, these models do not involve the Perdew–Burke–Ernzerhof (PBE) band gap as a feature. Our results demonstrate that machin...

First-principles study of electronic structure modulations in graphene on Ru(0001) by Au intercalation

First-principles calculations based on density functional theory are used to explore the electronic-structure modulations in graphene on Ru(0001) by Au intercalation. We first use a lattice-matched model to demonstrate that a substantial band gap is induced in graphene by sufficiently strong A-B sublattice symmetry breaking. This band gap opening occurs even in the absence of hybridization between graphene $\ensuremath{\pi}$ states and Au states, and a strong sublattice asymmetry is established for a small separation ($d$) between the graphene and Au layer, typically, $dl3.0\phantom{\rule{0.28em}{0ex}}\AA{}$, which can actually be achieved for a low Au coverage. In realistic situations, which are mimicked using lattice-mismatched models, graphene $\ensuremath{\pi}$ states near the Dirac point easily hybridize with nearby (in energy) Au states even for a van der Waals distance, $d\ensuremath{\sim}3.4\phantom{\rule{0.28em}{0ex}}\AA{}$, and this hybridization usually dictates a band gap opening in graphene. In that case, the top parts of the intact Dirac cones survive the hybridization and are isolated to form midgap states within the hybridization gap, denying that the band gap is induced by sublattice symmetry breaking. This feature of a band gap opening is similar to that found for the so-called ``first'' graphene layer on silicon carbide (SiC) and the predicted band gap and doping level are in good agreement with the experiments for graphene/Au/Ru(0001).