Prediction Of Electronic Structure Research Articles

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.

Quantum phase transitions in one-dimensional nanostructures: a comparison between DFT and DMRG methodologies.

In the realm of quantum chemistry, the accurate prediction of electronic structure and properties of nanostructures remains a formidable challenge. Density functional theory (DFT) and density matrix renormalization group (DMRG) have emerged as two powerful computational methods for addressing electronic correlation effects in diverse molecular systems. We compare ground-state energies ( ), density profiles ( ), and average entanglement entropies ( ) in metals, insulators and at the transition from metal to insulator, in homogeneous, superlattices, and harmonically confined chains described by the fermionic one-dimensional Hubbard model. While for the homogeneous systems, there is a clear hierarchy between the deviations, , and all the deviations decrease with the chain size; for superlattices and harmonic confinement, the relation among the deviations is less trivial and strongly dependent on the superlattice structure and the confinement strength considered. For the superlattices, in general, increasing the number of impurities in the unit cell represents lower precision in the DFT calculations. For the confined chains, DFT performs better for metallic phases, while the highest deviations appear for the Mott and band-insulator phases. This work provides a comprehensive comparative analysis of these methodologies, shedding light on their respective strengths, limitations, and applications. The DFT calculations were performed using the standard Kohn-Sham scheme within the BALDA approach. It integrated the numerical Bethe-Ansatz (BA) solution of the Hubbard model as the homogeneous density functional within a local-density approximation (LDA) for the exchange-correlation energy. The DMRG algorithms were implemented using the ITensor library, which is based on the matrix product states (MPS) ansatz. The calculations were performed until the energy reaches convergence of at least .

Mobility inhibition of arsenic in the soil: the role of green synthesized silica nanoparticles

The studies showed the effectiveness of green-synthesized SiO2NPs in mitigating the toxicity of Arsenic. Density Functional Theory (DFT) is a computational method used to determine electronic structure, energy gap, and toxicity prediction. Experimentally, silicon nanoparticles of 0 (S0) and 100% v/v (S100) were applied to the surface of the soil. 150 mL of Arsenic trioxide was applied twice at a rate of 0 (As0) and 3.2 g/mL (As3.2) at an interval of three weeks. Green synthesized SiO2NPs possessed a higher chemical potential (µ) and electrophilicity index; consequently, charges could be transferred and easily polarized. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) levels of the green synthesized SiO2NPs enable them to donate electrons and complex with arsenic, reducing their bioavailability and toxicity. Evidence from the studies further showed that SiO2NPs had buffered the soil acidity and electric conductivity, posing a high binding site and reactivity with exchangeable cations and micronutrients due to their smaller energy gap. Furthermore, the catalytic activities of the soil enzymes dehydrogenase (DHA) and peroxidase (POD) were greatly increased, which enhanced the electrostatic interaction between the SiO2NPs and As.

Review of the second charged-particle transport coefficient code comparison workshop

We report the results of the second charged-particle transport coefficient code comparison workshop, which was held in Livermore, California on 24–27 July 2023. This workshop gathered theoretical, computational, and experimental scientists to assess the state of computational and experimental techniques for understanding charged-particle transport coefficients relevant to high-energy-density plasma science. Data for electronic and ionic transport coefficients, namely, the direct current electrical conductivity, electron thermal conductivity, ion shear viscosity, and ion thermal conductivity were computed and compared for multiple plasma conditions. Additional comparisons were carried out for electron–ion properties such as the electron–ion equilibration time and alpha particle stopping power. Overall, 39 participants submitted calculated results from 18 independent approaches, spanning methods from parameterized semi-empirical models to time-dependent density functional theory. In the cases studied here, we find significant differences—several orders of magnitude—between approaches, particularly at lower temperatures, and smaller differences—roughly a factor of five—among first-principles models. We investigate the origins of these differences through comparisons of underlying predictions of ionic and electronic structure. The results of this workshop help to identify plasma conditions where computationally inexpensive approaches are accurate, where computationally expensive models are required, and where experimental measurements will have high impact.

An accurate prediction of electronic structure, mechanical stability and optical response of BaCuF3 fluoroperovskite for solar cell application

This study utilizes the first-principles modelling approach based on the density functional theory (DFT) framework to investigate the structural, mechanical, and optoelectronic properties of the BaCuF3 fluoroperovskite. Initially, the three distinct cubic structures of BaCuF3 were simulated based on the positioning of F atoms. This investigation aimed to determine the most stable configuration for BaCuF3. The structural stability of all compounds has been evaluated through the formation enthalpy calculations. In addition, the mechanical stability is assessed by the investigation of elastic stiffness constants. The mechanical stability of the α-BaCuF3 and β-BaCuF3 compounds has been observed. However, the γ-BaCuF3 compound is deemed unstable due to its failure to meet the Born-stability criterion (C44 < 0). The ductility of α-BaCuF3 and the brittleness of β-BaCuF3 have been determined by exploring Pugh's and passion ratios and the Cauchy pressure. The electronic characteristics have been evaluated by examining electronic band structure, density of states and partial density of states. The compounds α-BaCuF3 and β-BaCuF3 have been seen to have indirect and direct band gaps of 0.14 eV (M–Γ) and 1.22 eV (Γ–Γ), respectively. Several optical parameters have been calculated and compared. The materials that have been chosen exhibit significant optical conductivity and absorption coefficients when exposed to high-energy photons while also demonstrating transparency at lower incident photon energy ranges. Our investigations into the optical properties have determined that α-BaCuF3 and β-BaCuF3 exhibit favourable characteristics for solar cell implementation, high-frequency UV, and optoelectronic devices.

Prediction of electronic structure and nonlinear optical properties of zinc oxide nanostructures by experimental characterization and theoretical investigation

Prediction of electronic structure and nonlinear optical properties of zinc oxide nanostructures by experimental characterization and theoretical investigation

Real-Time Krylov Theory for Quantum Computing Algorithms

Quantum computers provide new avenues to access ground and excited state properties of systems otherwise difficult to simulate on classical hardware. New approaches using subspaces generated by real-time evolution have shown efficiency in extracting eigenstate information, but the full capabilities of such approaches are still not understood. In recent work, we developed the variational quantum phase estimation (VQPE) method, a compact and efficient real-time algorithm to extract eigenvalues on quantum hardware. Here we build on that work by theoretically and numerically exploring a generalized Krylov scheme where the Krylov subspace is constructed through a parametrized real-time evolution, which applies to the VQPE algorithm as well as others. We establish an error bound that justifies the fast convergence of our spectral approximation. We also derive how the overlap with high energy eigenstates becomes suppressed from real-time subspace diagonalization and we visualize the process that shows the signature phase cancellations at specific eigenenergies. We investigate various algorithm implementations and consider performance when stochasticity is added to the target Hamiltonian in the form of spectral statistics. To demonstrate the practicality of such real-time evolution, we discuss its application to fundamental problems in quantum computation such as electronic structure predictions for strongly correlated systems.

ARPES Signatures of Few-Layer Twistronic Graphenes.

Diverse emergent correlated electron phenomena have been observed in twisted-graphene layers. Many electronic structure predictions have been reported exploring this new field, but with few momentum-resolved electronic structure measurements to test them. We use angle-resolved photoemission spectroscopy to study the twist-dependent (1° < θ < 8°) band structure of twisted-bilayer, monolayer-on-bilayer, and double-bilayer graphene (tDBG). Direct comparison is made between experiment and theory, using a hybrid k·p model for interlayer coupling. Quantitative agreement is found across twist angles, stacking geometries, and back-gate voltages, validating the models and revealing field-induced gaps in twisted graphenes. However, for tDBG at θ = 1.5 ± 0.2°, close to the magic angle θ = 1.3°, a flat band is found near the Fermi level with measured bandwidth Ew = 31 ± 5 meV. An analysis of the gap between the flat band and the next valence band shows deviations between experiment (Δh = 46 ± 5 meV) and theory (Δh = 5 meV), indicative of lattice relaxation in this regime.

Fluorescent Azomethine by the Condensation of Sulfadiazine and 4-Chlorobenzaldehyde in Solution: Synthesis, Characterization, Solvent Interactions, Electronic Structure, and Biological Activity Prediction

The Schiff base was synthesized and characterized with Infrared, Raman, UV, fluorescence, and NMR studies. The present work is theoretically investigated using DFT/WB97XD/cc-pVTZ basis set. The local energy decomposition (LED) has been performed using PBE0-D3/deft-TZVP basis set and DLPNO-CCSD(T) coupled cluster level. The DFT was used to define the molecular structure and geometry of the compound. To identify the primary binding sites and weak interactions, compound was subjected to a series of topological analyses, including ELF, LOL, ALIE, and RDG. The UV-Visible spectrum was analyzed using the TD-SCF method. The DFT/WB97XD/cc-pVTZ was used to measure the HOMO-LUMO, MEP, and NLO. The NBO calculations investigate the intermolecular and intramolecular movement of charges, as well as the molecule stability. To determine whether the 4CLBDA molecule has the potential to become a drug, computational drug-likeness studies were conducted. The docking study done against the 6X7Q protein is performed and reported.

Prediction of the Ground-State Electronic Structure from Core-Loss Spectra of Organic Molecules by Machine Learning.

The core-loss spectrum reflects the partial density of states (PDOS) of the unoccupied states at the excited state and is a powerful analytical technique to investigate local atomic and electronic structures of materials. However, various molecular properties governed by the ground-state electronic structure of the occupied orbital cannot be directly obtained from the core-loss spectra. Here, we constructed a machine learning model to predict the ground-state carbon s- and p-orbital PDOS in both occupied and unoccupied states from the C K-edge spectra. We also attempted an extrapolation prediction of the PDOS of larger molecules using a model trained by smaller molecules and found that the extrapolation prediction performance can be improved by excluding tiny molecules. Besides, we found that using smoothing preprocess and training by specific noise data can improve the PDOS prediction for noise-contained spectra, which pave a way for the application of the prediction model to the experimental data.

A new tetragonal superhard carbon allotrope with unusual stress-strain behavior

We identified a carbon allotrope with tetragonal symmetry (P42/nnm, 12 atoms/cell), termed TC12. Unusual tensile behavior characterized by an enormous deformation was observed, leading to a phase transition from superhard TC12 to metallic T6 carbon. This could be related to planar recovery of the C2 atom during stretching of twisted six-membered rings along the crystal direction [100]. The tensile limit of TC12 along the [100] crystal direction is up to 62.5%, corresponding to the tensile strength of 129.96 GPa. The phonon dispersion spectra and elastic constant verified its dynamical and mechanical stabilities. TC12 is a metastable 3D carbon allotrope possessing remarkable mechanical properties: bulk modulus of 339 GPa together with hardness of 56.8 GPa. Electronic structure prediction showed that semiconductor TC12 has an indirect bandgap of 2.247 eV. The findings establish a unique type of carbon phase, which is suggested as a potential material for electronic and machining applications.

Electronic structure and detonation property prediction of pentazolate derivatives: Aminopentazole, diaminopentazole cations, azopentazole, and 1,2-diazopentazole

In this study, various polynitrogen pentazolate derivatives: aminopentazole, diaminopentazole cations, azopentazole (N12), and 1,2-diazopentazole (N14), were designed and computed. Based on quantum chemistry calculations and wavefunction analyses, the equilibrium geometry, bond orders, atoms-in-molecules (AIM) analysis, atomic charges, electrostatic potential distribution, frontier molecular orbitals, and many indices to represent aromaticity were determined and represented. In addition, their densities, heats of formation, detonation properties, and impact sensitivities were calculated using thermodynamics methods. These polynitrogen pentazolate derivatives could exhibit high density (1.668–1.965 g·cm−3); high heat of formation (2.96–15.86 kJ·g−1); outstanding detonation performance (D: 9070–12,243 m·s−1, P: 34.8–70.0 GPa); in particular, N14 (D: 12,243 m·s−1, P: 70.0 GPa); acceptable thermostability and sensitivity (h50: 12–49 cm). The excellent energetic performance demonstrates that polynitrogen pentazolate derivatives are promising energetic materials. The synthesis of aminopentazole and diaminopentazole cations may be achieved via an amination agent and the synthesis of N12 and N14 using an oxidizing agent.

Surface symmetry effect on the charge transfer at the black, blue, and green phosphorene/graphene interfaces

In the present study, a comparative prediction of atomic and electronic structure of black, blue, and green phosphorene/graphene heterostructures is presented using Density Functional Theory (DFT). The lowest total and interaction energies and highest charge transfer correspond to the blue phosphorene/graphene interface, followed by black and green phosphorene/graphene interfaces. This trend is the same for monolayer, AA-, and AB-stacked bilayer graphene. However, the charge transfer is more important to AB-stacked bilayer graphene at the interface with black and green phosphorene than to AA-stacked bilayer graphene. On another hand, for the charge transfer is more important from AA-stacked bilayer graphene to blue phosphorene, than from AB-stacked bilayer graphene. Besides, small bandgaps appear in phosphorene/bilayer graphene heterostructures, resulting from the symmetry breaking due to the charge difference between the two layers of bilayer graphene. These findings provide useful insights on energetic stability of graphene/phosphorene heterostructures with promising properties for future nanoelectronics devices.

DFT Method Used for Prediction of Molecular and Electronic Structures of Mn(VI) Macrocyclic Complexes with Porhyrazine/Phthalocyanine and Two Oxo Ligands.

By using the data of the DFT quantum chemical calculation in the OPBE/TZVP and B3PW91/TZVP levels, the possibility of the existence of a manganese(VI) heteroligand complex containing porphyrazine or its tetra[benzo] derivative (phthalocyanine) and two oxygen (O2-) ligands, which is still unknown for this element, is shown. The parameters of the molecular structure, multiplicity of the ground state, NBO analysis data and standard thermodynamic parameters (enthalpy ΔH0f, entropy S0f and Gibbs's energy ΔG0f of formation) of each of these metal macrocyclic compounds are presented. Additionally, it is noted that, based on the totality of structural data obtained by the above versions of the DFT method, the existence of a similar complex of manganese with di[benzo] derivative of porhyrazine and two oxygen (O2-) ligands seems doubtful.

Quantum-Chemical Prediction of Molecular and Electronic Structure of Carbon-Nitrogen Chemical Compound with Unusual Ratio Atoms: C(N20).

Using various versions of quantum-chemical calculation, namely four versions of density functional theory (DFT), (DFT B3PW91/TZVP, DFT M06/TZVP, DFT B3PW91/Def2TZVP, and DFT M06/Def2TZVP) and two versions of the MP method (MP2/TZVP and MP3/TZVP), the existence possibility of the carbon-nitrogen-containing compound having an unusual M: nitrogen ratio of 1:20, unknown for these elements at present, was shown. Structural parameters data are presented; it was noted that, as may be expected, CN4 grouping has practically a tetrahedral structure, and the chemical bond lengths formed by nitrogen atoms and a carbon atom in the frameworks of each of the calculation methods indicated above are equal to each other. Thermodynamical parameters, NBO analysis data, and HOMO/LUMO images for this compound are also presented. A good agreement between the calculated data obtained using the above three quantum-chemical methods was noticed, too.

Prediction of electronic structure and thermoelectric performance of bulk and monolayer BiSbSeTe2

Prediction of electronic structure and thermoelectric performance of bulk and monolayer BiSbSeTe2

Machine Learned Composite Methods for Electronic Structure Theory

Because of the prohibitive scaling of ab initio techniques for modeling chemical species with high accuracy, they are not generally tractable for large systems. It is therefore of considerable interest to develop high-accuracy computational models with low computational cost that can afford predictions of electronic structure and properties of macromolecular species. Composite methods, as first introduced by Pople [Pople, J. A.; Head-Gordon, M.; Fox, D. J.; Raghavachari, K.; Curtiss, L. A. J. Chem. Phys.1989, 90, 5622.], are an intuitive solution to this problem as they seek to systematically increase accuracy in model chemistries by taking advantage of favorable error cancellation among reasonably low-cost models. By linearly combining a series of carefully chosen model chemistries, the result of a prohibitive-scaling correlated model chemistry with a large basis set may be approximated with relatively good fidelity. However, the full extent to which the choice of low-cost models dictates the predictive accuracy of composite methods is not known, and a full exploration of all model chemistries would be advantageous for the design and validation of a generalizable composite method for widespread application. Here, we show that remarkable accuracy can be generally achieved with composite methods that are more judiciously constructed, leading to increased accuracy with significantly reduced computational cost. By designing a systematic procedure for the automated generation and assessment of over 10 billion unique composite methods, we have extensively explored the space of modern model chemistries to elucidate important design principles in the construction of reliable composite procedures. We anticipate our work to be the starting point in the pursuit of creative approaches to modeling large chemical systems with high accuracy by using novel combinatorial modeling.

Prediction of electronic structure and magnetic anisotropy of two-dimensional MSi2N4 (M = 3d transition-metal) monolayers

Two-dimensional (2D) transition metal nitrides have potential applications in novel low-dimensional spintronic devices due to their unique properties such as higher surface area and electrical conductivity than graphene. Recently, the successfully synthesized MoSi2N4 monolayer has attracted much attention. In order to improve its application, MSi2N4 monolayers are systematically analyzed in this work, where M represents 3d transition metal. The electronic structure and magnetic anisotropy of MSi2N4 monolayers are calculated by first-principles calculations. It is found that ScSi2N4 and VSi2N4 monolayers are half-metallic. TiSi2N4 and CrSi2N4 monolayers are semiconductors. MnSi2N4, FeSi2N4, CoSi2N4, NiSi2N4, CuSi2N4 and ZnSi2N4 monolayers are metallic. By applying strain to MSi2N4, part of the metallic monolayers can be transformed into half-metallic or semiconducting ones. The half-metallic VSi2N4 monolayer can be transformed into a metal or a semiconductor under biaxial strain. The magnetism of ZnSi2N4 disappears at compressive strain, while it transforms into a half-metallic at + 6 % tensile strain. Meanwhile, the magnetic anisotropy energy of ZnSi2N4 transformed from perpendicular magnetic anisotropy to in-plane magnetic anisotropy when tensile strains are applied. 2D materials with half-metallic abundance and perpendicular magnetic anisotropy have potential applications in spintronic devices.

First-principles prediction of asymmetric electronic structures, optoelectronic features, and efficiency for Sb2S3, Tl2S, TlSbS2, and TlSb3S5 compounds

In this study, the density functional theory calculations were used to calculate the electronic structures and optical properties of binary and ternary antimony chalcogenide compounds Sb2S3, Tl2S, TlSbS2, and TlSb3S5. The band structures and density of states have shown that the asymmetric densities in Sb2S3, TlSbS2, and TlSb3S5 compounds are due to the presence of 5s states of antimony-Sb. This asymmetric density is affected by the increase in the energy of the 3p valence state of the S element. The fundamental bandgaps were calculated, which revealed a direct semiconductor character in the middle of the Brillouin zone for TlSb3S5, whereas Sb2S3, Tl2S, and TlSbS2 exhibited an indirect semiconductor behavior. In addition, the changes in various optical quantities as a function of wavelength were also calculated and discussed to determine the optoelectronic efficiency of the studied compounds.

Graph representation-based machine learning framework for predicting electronic band structures of quantum-confined nanostructures

The application of machine learning (ML) to electronic structure theory enables electronic property prediction with ab initio accuracy. However, most previous ML models predict one or several properties of intrinsic materials. The prediction of electronic band structure, which embeds all the main electronic information, has yet to be deeply studied. This is a challenging task due to the highly variable inputs and outputs; the input materials may have different sizes and compositions, and the output band structures may have varying band numbers and k-point samplings. This task becomes even more difficult when quantum-confined nanostructures are considered, whose band structures are sensitive to the confinements applied. This paper presents an ML framework for predicting band structures of quantum-confined nanostructures from their geometries. Our framework introduces a graph convolutional network applicable to materials with varying compositions and geometries to extract their atoms’ local environment information. A learnable real-space Hamiltonian construction process then enables the utilization of the information to predict the electronic structure at any arbitrary k-point; the theoretical foundations introduced in this process help to capture and incorporate minor changes in quantum confinements into band structures, and endow the framework with the ability of few-shot learning. Taking an example of graphene nanoribbons, typical quantum-confined nanostructures, we show how the framework is constructed and its excellent performance on band structure prediction with a tiny data set. Our framework may not only provide a rapid yet reliable method for electronic structure determination but also enlighten the applications of graph representation to ML in related fields.