Plane-wave Basis Research Articles

Analytical treatment of the Yukawa screened Coulomb interaction in a plane-wave basis

The calculation of the many-electron (screened) Coulomb and exchange integrals is a very common task to perform in modern electronic structure theory. While an analytical treatment of the complete integrals is too complicated to be attempted directly, essential insight can be gained by focusing on the most significant contributions, as determined by a physical criterion. The monopole term of the screened Coulomb interaction is particularly important, since it defines the Hubbard interaction U among a set of localized electrons, which is an essential parameter for effective models of strongly correlated systems. Here, we derive an analytical solution for the matrix elements of the screened Coulomb interaction on a plane-wave basis, which is routinely used in the most common methods for electronic structure calculations. Screening is treated using a Yukawa potential, which is suitable to describe the valence electrons in the Fermi level region. For the solution of the integrals, the plane waves and the Yukawa potential are first expanded in Bessel functions of first and second kind. Then, by means of the lower and upper incomplete gamma functions, we are able to obtain closed-form integrals of a series that remains convergent for realistic parameters. Our exact solution for the radial integrals of the monopole term can find usage in plane-wave codes for electronic structure calculations, both as an output tool as well as within the computational cycle, as e.g., for many-body extensions of density-functional theory. Considering the importance of Bessel functions in solid state physics and electronic structure theory, it is also easy to foresee that our solution to the various integrals across this work may become useful to several other problems in the field.

A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements

Abstract We present a machine-learned (ML) model of kinetic energy for orbital-free density functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of group III–V elements. The functional is machine-learned with Gaussian process regression (GPR) from data computed with Kohn-Sham DFT with plane wave bases and local pseudopotentials. The dataset includes multiple phases of unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In. A total of 433 materials were used for training, and 18 strained structures were used for each material. Averaged (over the unit cell) kinetic energy density is fitted as a function of averaged terms of the 4th order gradient expansion and the product of the density and effective potential. The kinetic energy predicted by the model allows reproducing energy-volume curves around equilibrium geometry with good accuracy. We show that the GPR model beats linear and polynomial regressions. We also find that unary compounds sample a wider region of the descriptor space than binary and ternary compounds, and it is therefore important to include them in the training set; a GPR model trained on a small number of unary compounds is able to extrapolate relatively well to binary and ternary compounds but not vice versa.

Generalizing deep learning electronic structure calculation to the plane-wave basis.

Deep neural networks capable of representing the density functional theory (DFT) Hamiltonian as a function of material structure hold great promise for revolutionizing future electronic structure calculations. However, a notable limitation of previous neural networks is their compatibility solely with the atomic-orbital (AO) basis, excluding the widely used plane-wave (PW) basis. Here we overcome this critical limitation by proposing an accurate and efficient real-space reconstruction method for directly computing AO Hamiltonian matrices from PW DFT results. The reconstruction method is orders of magnitude faster than traditional projection-based methods to convert PW results to the AO basis, and the reconstructed Hamiltonian matrices can faithfully reproduce the PW electronic structure, thus bridging the longstanding gap between the AO basis deep learning electronic structure approach and PW DFT. Advantages of the PW methods, such as high accuracy, high flexibility and wide applicability, thus can be all integrated into deep learning electronic structure methods without sacrificing these methods' inherent benefits. This allows for the construction of large-scale and high-fidelity training datasets with the help of PW DFT results towards the development of precise and broadly applicable deep learning electronic structure models.

Dataset for carbon dioxide adsorption on lizardite

This paper presents data on the adsorption of CO2 on the (001) and (00 ) surface lizardite. The data were obtained from calculations using the density functional theory (DFT) method implemented in the CASTEP software package. The calculations were performed using the plane wave basis set and the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. A semi-empirical Grimme D2 correction was used to correctly account for dispersion interactions, which play an important role in physical adsorption processes. The data include optimized geometry and electronic properties of equilibrium states of adsorbed CO2 on (001) and (00 ) lizardite surfaces. They contain the most energetically favorable adsorption sites and estimates of the interaction strength between the adsorbate and adsorbent. The analysis of the electronic structure includes calculations of charge distribution, density of states (DOS), and visualizations of electron density difference isosurfaces. These data provide detailed insights into the redistribution of electron density during adsorption and the nature of the bonds formed. All data, including optimized crystal structures, electronic properties, and calculation results, are available in formats that can be read by commonly available text editors and specialized atomic structure visualization and analysis software. The presented dataset can serve as a foundation for further studies on the interactions of various molecules with the lizardite surface.

Stability analysis of SnO2 surfaces using First-Principles computational methods

Abstract This study focuses on investigating the structural and electronic properties of the most stable faces of SnO2 using the Quantum Espresso software. Structural properties were found applying the generalized gradient approximation (GGA-PBE) with ultrasoft pseudopotentials and plane wave basis sets. Methodology involved determining the cutoff of the set of plane waves, selecting appropriate k-points to represent the first Brillouin zone, and optimized the lattice parameters (a and c). Models were generated for each studied surface, with the most optimal being the one with the lowest energy. These results are based on the periodicity of infinite sets of surfaces, increasing the gaps of the vacuum layers. As a result of the calculations, a discrepancy of less than 1% was observed in the lattice parameters compared to previous publications. Furthermore, it was found that the (110) is the most stable surface, in agreement with results published using VASP.

PW-SMD: A Plane-Wave Implicit Solvation Model Based on Electron Density for Surface Chemistry and Crystalline Systems in Aqueous Solution.

Electron density-based implicit solvation models are a class of techniques for quantifying solvation effects and calculating free energies of solvation without an explicit representation of solvent molecules. Integral to the accuracy of solvation modeling is the proper definition of the solvation shell separating the solute molecule from the solvent environment, allowing for a physical partitioning of the free energies of solvation. Unlike state-of-the-art implicit solvation models for molecular quantum chemistry calculations, e.g., the solvation model based on solute electron density (SMD), solvation models for systems under periodic boundary conditions with plane-wave (PW) basis sets have been limited in their accuracy. Furthermore, a unified implicit solvation model with both homogeneous solution-phase and heterogeneous interfacial structures treated on equal footing is needed. In order to address this challenge, we developed a high-accuracy solvation model for periodic PW calculations that is applicable to molecular, ionic, interfacial, and bulk-phase chemistry. Our model, PW-SMD, is an extension of the SMD molecular solvation model to periodic systems in water. The free energy of solvation is partitioned into the electrostatic and cavity-dispersion-solvent structure (CDS) contributions. The electrostatic contributions of the solvation shell surrounding solute structures are parametrized based on their geometric and physical properties. In addition, the nonelectrostatic contribution to the solvation energy is accounted for by extending the CDS formalism of SMD to incorporate periodic boundary conditions. We validate the accuracy and robustness of our solvation model by comparing predicted solvation free energies against experimental data for molecular and ionic systems, carved-cluster composite energetic models of solvated reaction energies and barriers on surface systems, and deep-learning-accelerated ab initio molecular dynamics (AIMD). Our developed periodic implicit solvation model shows significantly improved accuracy compared to previous work (namely, solvation models in aqueous solution) and can be applied to simulate solvent effects in a wide range of surface and crystalline materials.

Transfer learning relaxation, electronic structure and continuum model for twisted bilayer MoTe2

Large-scale moiré systems are extraordinarily sensitive, with even minute atomic shifts leading to significant changes in electronic structures. Here, we investigate the lattice relaxation effect on moiré band structures in twisted bilayer MoTe2 with two approaches: (a) large-scale plane-wave basis first principle calculation down to 2.88°, (b) transfer learning structure relaxation + local-basis first principles calculation down to 1.1°. We use two types of van der Waals corrections: the D2 method of Grimme and the density-dependent energy correction, and find that the density-dependent energy correction yields a continuous evolution of bandwidth with twist angles. Based on the above results. we develop a complete continuum model with a single set of parameters for a wide range of twist angles, and perform many-body simulations at ν = −1, −2/3, −1/3.

Interaction of Titanium Atoms with the Surface of Perfect and Defective Carbon Nanotubes

The dispersion of metal atoms over the surface of 1D and 2D carbon systems is the most affordable way to control their properties, which are attractive for many applications in electronics, power engineering, and catalysis. In this work, the features of the interaction of titanium atoms with the surface of carbon nanotubes, caused by various structural defects on these surfaces, were studied by first-principles computer simulation based on the density functional theory. Nanotubes (7, 7) and (11, 0) with similar diameters (≈1 nm) but different types of conductivity, metallic and semiconductor, respectively, were chosen for the study. Three types of defects were studied: a single vacancy, a double vacancy, and a topological defect. Two possible orientations of each type of defect relative to the tube axis were considered. We mainly used the basis of atomic-like orbitals (the SIESTA package) and in some test calculations also the basis of plane waves (the VASP package). Computational experiments have shown that the binding energy of Ti atoms with a defect-free nanotube is always lower than with defective ones, regardless of the used approximation for the exchange-correlation functional (LDA or GGA). The binding energies predicted in the LDA approximation are noticeably higher than in the GGA approximation (up to ~15% for the (7, 7) tube and up to ~50% for the (11, 0) tube). The strongest coupling occurs when the titanium atom is adsorbed on a nanotube with a single vacancy. The resulting configuration can be considered as a defect in the substitution of one carbon by a titanium atom.

Enhanced figure of merit in two-dimensional ZrNiSn nanosheets for thermoelectric applications

A novel two-dimensional (2D) half-Heusler ZrNiSn nanosheet for thermoelectric applications was designed from bulk half-Heusler ZrNiSn through first-principles calculation. Investigation of bulk half-Heusler and 2D nanosheet ZrNiSn was performed with the Quantum Espresso code based on a density functional theory plane wave basis set. Electronic band structure and density of states calculations were used to study the confinement effects. On moving from bulk to 2D a change of structure is observed from face-centered cubic to trigonal due to confinement effects. The semiconducting nature of bulk ZrNiSn is undisturbed while moving to a 2D nanosheet; however, the band gap is widened from 0.46 to 1.3 eV due to the restricted motion of electrons in one direction. Compared with bulk ZrNiSn, 2D nanosheets were found to have a higher Seebeck coefficient a lower thermal conductivity and higher figure of merit, which makes 2D ZrNiSn nanosheets suitable for thermoelectric applications. Atomically thin 2D structures with a flat surface have the potential to form van der Waals heterojunctions, paving the way for device fabrication at the nanoscale level.

A robust, simple, and efficient convergence workflow for GW calculations

A robust, simple, and efficient convergence workflow for GW calculations in plane-wave-based codes is derived from more than 7000 GW calculations on a diverse dataset of 70 semiconducting and insulating solids divided into 60 bulk and 10 2D materials. The workflow can significantly accelerate material screening projects and high-precision single-system studies. Our method is based on two main results: The convergence of the two interdependent parameters in the numerical implementation of the dynamically screened Coulomb interaction W in a plane-wave basis set is accelerated by a ‘cheap first, expensive later’ coordinate search that maintains the same accuracy as a state-of-the-art convergence algorithm, but converges faster. In addition, we empirically establish the practical independence of the k-point grid and the aforementioned parameterization of W. Incorporating both results into one workflow dramatically speeds up convergence.

Tuning the potentials of aluminum phosphide nanotube photocatalyst for wastewater treatment through band gap engineering: a DFT study

The photocatalytic properties of semiconductor materials, which are controllable through the design of the bandgap structure, make them a promising catalyst for wastewater treatment. This work investigated the photocatalytic properties of single-walled aluminum phosphide nanotube (SWAlPNT) doped with different concentrations of boron (B) atoms for wastewater treatment. Analysis of the structural, electronic and optical properties of the SWAlPNT photocatalyst was performed using the density functional theory approach in terms of plane wave basis set and pseudopotential. SWAlPNT was found to be stable to B doping with 3.6% and 7.1% concentrations. The obtained formation energy values of 12.33 eV, 12.00 eV and 11.98 eV and also cohesive energies of −0.82, eV, −0.75 eV and 0.79 eV for pristine, 3.6% and 7.1% B-doped SWAlPNTs, revealed the systems’ well mechanical and thermodynamic stabilities. Results also revealed that cohesive energy decreases with an increase in concentration of B dopant, which significantly enhances efficient thermal stability. Electronic band gap calculations revealed that pristine SWAlPNT demonstrated a direct band gap value of 0.2 eV. Due to B doping, an indirect band gap value of 1.4 eV was obtained with 3.6% B-doped SWAlPNT, which agreed well with band gaps of other photocatalysts used for wastewater purification. Analysis using optical absorption spectra revealed that 3.6% B-doped system absorbs visible light while 7.1% doped system absorbs both visible and ultraviolet light. This study found both 3.6% and 7.1% B-doped SWAlPNT as suitable photocatalysts for wastewater treatment under solar irradiation, with the 3.6% B-doped system demonstrating relatively better performance for wastewater treatment.

Chemical bonding and electronic properties along Group 13 metal oxides

ContextThe present work provides a systematic theoretical analysis of the nature of the chemical bond in Al2O3, Ga2O3, and In2O3 group 13 cubic crystal structure metal oxides. The influence of the functional in the resulting band gap is assessed. The topological analysis of the electron density provides unambiguous information about the degree of ionicity along the group which is linearly correlated with the band gap values and with the cost of forming a single oxygen vacancy. Overall, this study offers a comprehensive insight into the electronic structure of metal oxides and their interrelations. This will help researchers to harness information effectively, boosting the development of novel metal oxide catalysts or innovative methodologies for their preparation.MethodsPeriodic density functional theory was used to predict the atomic structure of the materials of interest. Structure optimization was carried out using the PBE functional, using a plane wave basis set and the PAW representation of the atomic cores, using the VASP code. Next, the electronic properties were computed by carrying out single point calculations employing PBE, PBE + U functionals using VASP and also with PBE and the hybrid HSE06 functionals using the FHI-AIMS software. For the hybrid HSE06, the impact of the screening parameter, ω, and mixing parameter, α, on the calculated band gap has also been assessed.

Extending the limit of LR-TDDFT on two different approaches: Numerical algorithms and new Sunway heterogeneous supercomputer

First-principles time-dependent density functional theory (TDDFT) is a powerful tool to accurately describe the excited-state properties of molecules and solids in condensed matter physics, computational chemistry, and materials science. However, a perceived drawback in TDDFT calculations is its ultrahigh computational cost O(N5∼N6) and large memory usage O(N4) especially for plane-wave basis set, confining its applications to large systems containing thousands of atoms. Here, we present a massively parallel implementation of linear-response TDDFT (LR-TDDFT) and accelerate LR-TDDFT in two different aspects: (1) numerical algorithms on the X86 supercomputer and (2) optimizations on the heterogeneous architecture of the new Sunway supercomputer. Furthermore, we carefully design the parallel data and task distribution schemes to accommodate the physical nature of different computation steps. By utilizing these two different methods, our implementation can gain an overall speedup of 10x and 80x and efficiently scales to large systems up to 4096 and 2744 atoms within dozens of seconds.

Complex-Valued K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Large-Scale Hybrid Functional Enabled Ab Initio Molecular Dynamics Simulations within Plane Waves.

K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition for hybrid functional electronic structure calculations. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled ab initio molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals. Here, we propose an improved weight function defined as the sum of the square modulus of complex-valued Kohn-Sham orbitals in K-means clustering for hybrid AIMD simulations. Numerical results demonstrate that the K-means algorithm with a new weight function yields smoother and more delocalized interpolation sampling points, resulting in smoother energy potential, smaller energy drift, and longer time steps for hybrid AIMD simulations compared to the previous weight function used in the real-valued K-means algorithm. In particular, we find that this improved algorithm can obtain more accurate oxygen-oxygen radial distribution functions in liquid water molecules and a more accurate power spectrum in crystal silicon dioxide compared to the previous K-means algorithm. Finally, we describe a massively parallel implementation of this ISDF decomposition to accelerate large-scale complex-valued hybrid AIMD simulations containing thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on modern supercomputers.

Polarization characteristics and structural modifications of Cu nanoparticles under high electric fields

High electric fields affect the diffusion dynamics of atoms on a metal surface, causing biased surface diffusion that possibly leads to the growth of intensively field emitting protrusions and consequent vacuum breakdown (VBD). The scientific understanding of this process, as well as other fundamental VBD initiation mechanisms, is far from complete. Here we investigate the exact atomic behaviour of metal surfaces exposed to extremely high electric fields using density functional theory (DFT). Previous theories describe the field-surface dynamics in terms of the effective dipole moments and polarizability of surface atoms, disregarding higher-order (hyperpolarizability) terms. The validity of this approximation has been evaluated only for electric fields up to 3 GV/m, due to computational limitations of the plane-wave DFT basis used in previous works. In this work, we test the validity of this approximation for a much wider field range, relevant for VBD and field emission (FE), using Cu nanoparticles as our test structures. We find that although such high fields can change the entire structure of Cu nanoparticles, their energetics are described very precisely by the permanent dipole moment and polarizability terms. Thus, we show that neglecting the hyperpolarizability terms is valid even for field values that exceeds the range that is relevant for intense FE and VBD. This work lays a solid foundation for further developing atomic-level simulation models for electric field-induced surface diffusion on metal surfaces and its effects on protrusion growth and VBD initiation.

Effect of hydrostatic pressure on opto-electronic, elastic and thermoelectric properties of the double perovskites Rb<sub>2</sub>SeX<sub>6</sub>(X=Cl,Br): a DFT study

Double perovskites find applications across a diverse range of situations and varying pressure conditions. In this work, Quantum ESPRESSO code with a plane wave basis set was used to study the opto-electronic, elastic, and thermoelectric properties of Rb2SeX6 (X=Cl, Br) double perovskites under hydrostatic pressure (0 - 8 GPa). Perdew-Burke-Ernzerhof for Solids (PBESol) with generalized gradient approximation (GGA) was used as exchange-correlation functional. The band gap values of the materials decrease under hydrostatic pressure. Rb2SeCl6 has a band gap value of 2.44 eV at 0 GPa, 2.21 eV at 2 GPa. Above 2 GPa, the material has a metallic nature. Rb2SeBr6 has a band gap value of 1.56 eV at 0 GPa, but has a metallic nature under hydrostatic pressure (2 GPa to 8 GPa). The optical properties results indicate that the materials exhibit maximum absorption, high reflectivity, low optical loss in the visible and ultraviolet regions, good optical conductivity, and a refractive index suitable for use in opto-electronic applications. The materials are confirmed to be mechanically stable under all the hydrostatic pressure values studied. Electrical conductivity, thermal conductivity, and Seebeck coefficient (S ) values of the studied materials increase with an increase in hydrostatic pressure and temperature. The maximum value of S for Rb2SeBr6 is 0.248 x 103 (m V/k), while for Rb2SeCl6, maximum S = 0.175 x 103 (m V/k). The positive values of S suggest that the predominant charge carriers of Rb2SeCl6/Br6 are holes. Also, Rb2SeBr6 has a figure of merit (ZT) value of 3.44, while for Rb2SeCl6, ZT = 1.07. Since the values of ZT are greater than unity, the two double perovskite materials have good ZT values for thermoelectric device engineering. The results also suggest that Rb2SeBr6 is a better thermoelectric material than Rb2SeCl6.

Machine Learning K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Accurate and Efficient Cubic-Scaling Exact Exchange Plus Random Phase Approximation within Plane Waves.

The exact-exchange plus random-phase approximation (EXX+RPA) method has emerged as a crucial tool for precisely characterizing electronic structures in molecular and solid systems. We present an accurate and efficient implementation of EXX+RPA calculations that scale cubically and are conducted within plane waves. Our approach incorporates the interpolative separable density fitting (ISDF) algorithm, effectively mitigating the computational challenges associated with the plane wave basis set. To overcome the constraints of the conventional ISDF algorithm, characterized by the exceptionally high prefactor in QR factorization for interpolation point selection, we introduce an enhanced machine learning K-means method. This method incorporates a novel empirical weight function called "SSM+" for more precise interpolation point selection, capturing physical information more accurately across diverse systems. Our machine learning approach offers a quasiquadratic scaling alternative, effectively replacing the computationally demanding cubic-scaling QRCP algorithm in plane-wave-based EXX+RPA calculations. Furthermore, we enhance the method's capabilities by optimizing GPU acceleration using MATLAB's integrated GPU toolkit. In particular, our approach reduces the computational scaling of χ0 from 3.80 to 2.13 and the overall computational scaling of EXX from 2.74 to 2.10. We achieve a remarkable GPU acceleration speedup of up to 35×. Regarding CPU computation time, the standard quartic-scaling method requires 22 h to compute Si128, while QRCP completes the calculation in only around 1 h, achieving a speedup up to 20×. However, the utilization of the K-means algorithm reduces the time to 800 s, a substantial improvement of 100× compared to the standard algorithm. By employing the K-means algorithm, the computational time for interpolative point calculation using QRCP decreases from 1 h to 1 min, resulting in a 55× speed increase. With this improved algorithm, we successfully computed the dissociation curve of H2 and the equilibrium polyynic geometry of C18 molecules.

Total absorption spectrum of benzene aggregates obtained from two different approaches.

The study of molecular aggregation effects on the electronic spectrum is essential for the development of optoelectronic devices. However, investigating the entire valence absorption spectrum of aggregates using quantum mechanical methods is a challenging task. In this work, we perform systematic simulations of the absorption spectrum of benzene molecular clusters up to 35 eV applying two approaches based on time-dependent density functional theory. The results show that depending on the dimer packing, different energy shifts occur for the symmetry allowed [Formula: see text] transition, in comparison to the monomer. The transition intensity increases for the band around 6 eV for larger aggregates from the monomer to dimers and tetramer, indicating the occurrence of the symmetry forbidden (in [Formula: see text] point group) [Formula: see text] [Formula: see text] transition. The benzene crystal exhibits a large redshift following the experimental spectrum. Also, the continuum regions of all spectra show a good agreement with the experiments both in gas and solid phases. Geometry optimization of the monomer was carried out with Gaussian 09 software using the PBE0/def2-TZVP level of theory. We used dimers and tetramer molecular geometries extracted from the experimental crystal structure. The absorption spectra were directly obtained by the Liouville-Lanczos TDDFT approach with plane waves basis set or indirectly by TDDFT pseudo-spectra calculated in a [Formula: see text] basis followed by analytic continuation procedure to obtain complex polarizability. The former is available at Quantum ESPRESSO, and the latter was calculated using Gaussian 09 with the post-processing performed with a code previously developed in our group.

Insights into the Electronic, Optical, and Anti-Corrosion Properties of Two-Dimensional ZnO: First-Principles Study

The electronic, optical, and anticorrosion properties of planer ZnO crystal and quantum dots are explored using density functional theory calculations. The calculations for the finite ZnO quantum dots were performed in Gaussian 16 using the B3LYP/6-31g level of theory. The periodic calculations were carried out using VASP with the plane wave basis set and the PBE functional. The subsequent band structure calculations were performed using the hybrid B3LYP functional that shows accurate results and is also consistent with the finite calculations. The considered ZnO nanodots have planer hexagonal shapes with zigzag and armchair terminations. The binding energy calculations show that both structures are stable with negligible deformation at the edges. The ZnO nanodots are semiconductors with a moderate energy gap that decreases when increasing the size, making them potential materials for anticorrosion applications. The values of the electronic energy gaps of ZnO nanodots are confirmed by their UV-Vis spectra, with a wide optical energy gap for the small structures. Additionally, the calculated positive fraction of transferred electrons implies that electron transfer occurs from the inhibitor (ZnO) to the metal surface to passivate their vacant d-orbitals, and eventually prevent corrosion. The best anti-corrosion performance was observed in the periodic ZnO crystal with a suitable energy gap, electronegativity, and fraction of electron transfer. The effects of size and periodicity on the electronic and anticorrosion properties are also here investigated. The findings show that the anticorrosion properties were significantly enhanced by increasing the size of the quantum dot. Periodic ZnO crystals with an appropriate energy gap, electronegativity, and fraction of electron transfer exhibited the optimum anticorrosion performance. Thus, the preferable energy gap in addition to the most promising anticorrosion parameters imply that the monolayer ZnO is a potential candidate for coating and corrosion inhibitors.

Tetraoxa[8]circulene monolayer as hydrogen storage material: model with Boys–Bernardi corrections within density functional theory

The parameters of molecular hydrogen adsorption on a tetraoxa[8]circulene monolayer were studied using the density functional theory with dispersion interaction corrections (semi-empirical and analytical). The calculations were carried out using two different approaches to the system wave function representation: atomic-like orbital basis set and plane wave basis. Utilizing a less computationally expensive pseudoatomic basis, it is possible to obtain results for molecular hydrogen adsorption consistent with values calculated with plane waves if the atomic-like basis is optimized and basis set superposition error is corrected for both hydrogen binding energy and geometrical characteristics. Otherwise, the H2 binding energy will be overestimated by 4–6 times (sometimes even more, by 20); and the hydrogen-monolayer distance will be underestimated by 10-20%. The obtained optimized parameters of the pseudoatomic basis set can be used for further study of the modified forms of the tetraoxa[8]circulene monolayer. Moreover, our calculations showed that the hydrogen binding to a pristine tetraoxa[8]circulene monolayer is predominantly van der Waals with an energy of 60–90 meV, which is several times less than the desired range of 200–600 meV. To achieve such values, it will be necessary to modify the surface of the monolayer, creating more active sorption cites, for example, by decorating it with metals or applying structural defects.