Electronic Structure Simulations Research Articles

Simulation of interlayer coupling for electroactive covalent organic framework design.

Porous, stacked two-dimensional covalent organic frameworks (2D COFs) bearing semiconducting linkers can support directional charge transfer across adjacent layers of the COF. To better inform the current and possible future design rules for enhancing electron and hole transport in such materials, an understanding of how linker selection and functionalization affects interlayer electronic couplings is essential. We report electronic structure simulation and analysis of electronic couplings across adjacent linker units and to encapsulated species in functionalized electroactive 2D COFs. The detailed dependence of these electronic couplings on interlayer interactions is examined through scans along key interlayer degrees of freedom and through configurational sampling from equilibrium molecular dynamics on semiempirical potential energy surfaces. Beyond affirming the sensitivity of the electronic coupling to interlayer distance and orientation, these studies offer guidance toward linker functionalization strategies for enhancing charge carrier transport in electroactive 2D COFs.

The Importance of Reaction Energy in Predicting Chemical Reaction Barriers with Machine Learning Models.

Improving our fundamental understanding of complex heterocatalytic processes increasingly relies on electronic structure simulations and microkinetic models based on calculated energy differences. In particular, calculation of activation barriers, usually achieved through compute-intensive saddle point search routines, remains a serious bottleneck in understanding trends in catalytic activity for highly branched reaction networks. Although the well-known Brønsted-Evans-Polyani (BEP) scaling - a one-feature linear regression model - has been widely applied in such microkinetic models, they still rely on calculated reaction energies and may not generalize beyond a single facet on a single class of materials, e. g., a terrace sites on transition metals. For highly branched and energetically shallow reaction networks, such as electrochemical CO2 reduction or wastewater remediation, calculating even reaction energies on many surfaces can become computationally intractable due to the combinatorial explosion of states that must be considered. Here, we investigate the feasibility of activation barrier prediction without knowledge of the reaction energy using linear and nonlinear machine learning (ML) models trained on a new database of over 500 dehydrogenation activation barriers. We also find that inclusion of the reaction energy significantly improves both classes of ML models, but complex nonlinear models can achieve performance similar to the simplest BEP scaling when predicting activation barriers on new systems. Additionally, inclusion of the reaction energy significantly improves generalizability to new systems beyond the training set. Our results suggest that the reaction energy is a critical feature to consider when building models to predict activation barriers, indicating that efforts to reliably predict reaction energies through, e. g., the Open Catalyst Project and others, will be an important route to effective model development for more complex systems.

Magnetic exchange interactions at the proximity of a superconductor

Interfacing magnetism with superconductivity gives rise to a wonderful playground for intertwining key degrees of freedom: Cooper pairs, spin, charge, and spin–orbit interaction, from which emerge a wealth of exciting phenomena, fundamental in the nascent field of superconducting spinorbitronics and topological quantum technologies. Magnetic exchange interactions (MEIs), being isotropic or chiral such as the Dzyaloshinskii–Moriya interactions, are vital in establishing the magnetic behavior at these interfaces as well as in dictating not only complex transport phenomena, but also the manifestation of topologically trivial or non-trivial objects. Here, we propose a methodology enabling the extraction of the tensor of MEI from electronic structure simulations accounting for superconductivity. We apply our scheme to the case of a Mn layer deposited on Nb(110) surface and explore proximity-induced impact on the MEI. The latter are weakly modified by a realistic electron-phonon coupling. However, tuning the superconducting order parameter, we unveil potential change of the magnetic order accompanied with chirality switching, as induced by the interplay of spin-orbit interaction and Cooper pairing. Owing to its simple formulation, our methodology can be readily implemented in state-of-the-art frameworks capable of tackling superconductivity and magnetism. We thus foresee implications in the simulations and prediction of topological superconducting bits as well as of cryogenic superconducting hybrid devices involving magnetic units.

Random Green’s Function Method for Large-Scale Electronic Structure Calculation

We report a linear-scaling random Green’s function (rGF) method for large-scale electronic structure calculation. In this method, the rGF is defined on a set of random states and is efficiently calculated by projecting onto Krylov subspace. With the rGF method, the Fermi–Dirac operator can be obtained directly, avoiding the polynomial expansion to Fermi–Dirac function. To demonstrate the applicability, we implement the rGF method with the density-functional tight-binding method. It is shown that the Krylov subspace can maintain at small size for materials with different gaps at zero temperature, including H2O and Si clusters. We find with a simple deflation technique that the rGF self-consistent calculation of H2O clusters at T = 0 K can reach an error of ∼ 1 meV per H2O molecule in total energy, compared to deterministic calculations. The rGF method provides an effective stochastic method for large-scale electronic structure simulation.

Pyrazinoquinoxaline graphdiyne: A novel high-capacity anode material for metal-ion batteries

The rapid expansion of the electronics industry and the growing need for advanced energy storage solutions have driven the quest for rechargeable batteries with improved capacity and prolonged lifetimes. In this context, significant attention has been directed towards the exploration of two-dimensional (2D) materials, particularly carbon nanosheets such as Graphdiyne, owing to their compelling electrical, optical, mechanical, and chemical properties. The recent achievement in producing pyrazinoquinoxaline graphdiyne (PQ-GDY) nanosheets, distinguished by their remarkable stability and exceptional physical properties, has ignited our research curiosity, prompting an exploration of their potential suitability as anode materials for lithium (Li), sodium (Na), calcium (Ca), and magnesium (Mg) ion batteries. This investigation is conducted using first-principle electronic structure simulations. Drawing from our rigorous theoretical analysis, pyrazinoquinoxaline graphdiyne (PQ-GDY) has demonstrated favorable electrode properties, positioning it as a promising candidate for potential utilization in Li, Na, and Ca ion batteries. Notably, the pyrazinoquinoxaline graphdiyne rectangular (PQ-GDY-Rec) material demonstrates remarkable storage capacities for Li, Na, and Ca ions, reaching values of 1938 mAh/g, 1716 mAh/g, and 830.60 mAh/g, respectively. These findings position PQ-GDY-Rec as a highly promising material for application in metal-ion batteries, suggesting new avenues for the advancement of rechargeable batteries with significantly enhanced storage capabilities.

Integrative approach of machine learning and symbolic regression for stability prediction of multicomponent perovskite oxides and high-throughput screening

This work unfolds a robust and interpretable strategy for evaluating the stability and potential photovoltaic application of 6526 multicomponent perovskite oxides, employing a synergetic methodology that intertwines advanced machine learning (ML) algorithms and symbolic regression based on genetic programming. Initially, ML algorithms, namely XGBoost, LightGBM, and random forest, were harnessed, with elemental oxidation state and electronegativity serving as input features, achieving R2 values of 0.98, 0.98, and 0.74, respectively, on the test set for predicting the formation enthalpy, a criterion for perovskite stability. Despite the amplified interpretability offered by SHAP analysis, the inherent “black-box” nature of ML obfuscates a transparent understanding of intrinsic relations between input features and performance. To surmount this, symbolic regression introduced not only elucidates a clear functional relationship between input features and perovskite stability but also achieves a commendable R2 of 0.79 on the test set. Subsequent high-throughput screening, based on perovskite stability ranking, designated the top 500 stable perovskites for band gap calculation using the PBE functional, wherein DyNdHf2O6, CeEuAl2O6, and CeSrAl2O6 emerged as potential candidates for photovoltaic applications and were subjected to further electronic structure simulations employing the HSE06 functional, encompassing density of states, band structure, charge density, and optical absorption spectra. Ultimately, CeEuAl2O6, boasting an optical direct bandgap of 2.31 eV and minimal electron-hole wavefunction overlap, stands out as the prime choice for photovoltaic materials. This research not only pioneers the exploration of enhancing the interpretability of ML but also propels theoretical guidance for the evolution of photovoltaic cells by bridging predictive modeling with high-throughput screening.

Perturbative variational quantum algorithms for material simulations

Reducing circuit depth is essential for implementing quantum simulations of electronic structure on near-term quantum devices. In this work, we propose a variational quantum eigensolver (VQE) based perturbation theory (PT) algorithm to accurately simulate electron correlation of periodic materials with shallow ansatz circuits, which are generated from adaptive derivative-assembled pseudo-trotter or qubit-excitation-based VQE calculations using a loose convergence criteria. Here, the major part of the electron correlation is described using the VQE ansatz circuit and the remaining correlation energy is described by either multireference or similarity transformation-based PT. Numerical results demonstrate that the new algorithms are able to accurately describe electron correlation of the LiH crystal with only one circuit parameter, in contrast with ~30 parameters required in the adaptive VQE to achieve the same accuracy. Meanwhile, for fixed-depth ansätze, e.g. unitary coupled cluster, we demonstrate that the VQE-based PT provides an appealing scheme to improve their accuracy.

Doping effect on electronic and magnetic properties of Ag-doped single-walled (6,0) GaN nanotubes: First-principles study

The electronic and magnetic properties of GaN wurtzite, un-doped, and Ag-doped single-walled (6,0) chiral GaN nanotubes (SWGaNNT) were investigated using Density Functional Theory and Local Spin Density Approximation methods within Hubbard U semiempirical correction (DFT-LSDA + U). In a consecutive DFT-LSDA + U electronic structure simulation, the effects of defects on the nature and origin of ferromagnetism (FM) are studied for an Ag-doped SWGaNNT system. For the Ag-doped GaN nanotube configurations, the energy gap decreases with increasing concentration of impurity. The total energy calculations for doped systems show the stability of the ferromagnetic phase. In the case of the Ag-doping GaN NT system, the wide energy gap is decreasing and the total magnetic moment of this system is ∼2.0 μB. From first-principles calculations, an Ag-doped GaN nanosystem can be made into a magnetic ferromagnetic material, and it is an important candidate for spintronics device applications.

Tetrahedrite copper antimony sulfide (Cu12Sb4S13) thin films for photovoltaic and self-powered photodetector applications grown by single step ultrasonic spray pyrolysis

This work reports in situ growth of tetrahedrite Cu12Sb4S13 thin films via ultrasonic spray pyrolysis using a non-aqueous solution containing CuCl2, SbCl3 and C2H5NS. The thin films deposited by varying S/(Cu + Sb) ratios in the precursor solution were studied using X-ray diffraction and Raman spectroscopy to evaluate the phase formation. Optical characterization showed a direct band gap of nearly 1.5 eV, consistent with the theoretical simulation of electronic band structure and optical spectra based on First principles calculations. Cu12Sb4S13 thin films formed as a function of S/(Cu + Sb) precursor ratio spans semiconducting (p) to semi-metallic (p+) behavior, confirmed by Hall effect. Photodetection properties of the p type Cu12Sb4S13 films showed a spectral response ranging from UV to visible and NIR wavelengths. Both p and p+ thin films were incorporated into photovoltaic structures using CdS as window layer, resulting: Voc = 0.273–0.398 V, Jsc = 2.5–16.3 mAcm−2, FF = 32–35% and efficiency = 0.33–1.4%. These heterostructures also performed as self-powered photodiodes for UV–Vis-NIR wavelength detection. This is the first report of a single-step synthesis of phase pure Cu12Sb4S13 thin films and their performance as self-biased photodiode and photovoltaic devices.

A first-principles study of the mechanical and physical properties of Ni3Snx intermetallic compounds for high-temperature power device packaging

Ni–Sn intermetallic compound is a highly promising high-temperature packaging material for power devices, and it is very necessary to understand and control the physical and mechanical properties of Ni–Sn intermetallic compounds(IMCs). However, the current research mainly focuses on the rapid preparation of such packaging solder joints, and there is little research on the microstructure, physical and mechanical properties of alloys. In this study, the first-principle and quasi-harmonic Debye models were used to analyze the mechanical characteristics, electronic structure, thermal conductivity, and linear expansion coefficient of three IMCs (Ni3Sn, Ni3Sn2, and Ni3Sn4) of the Ni–Sn system. The results show that Ni3Sn has the highest modulus and fracture toughness and has the best elastic match with Ni. The welded joints of Ni/Ni3Sn/Ni structures can effectively prevent the generation of microcracks under the stress field. They're all mixed-bond compounds, according to the results of electronic structure simulations, and Ni3Sn4 has the most ionic and covalent bonds, so its fracture toughness is the lowest and its anisotropy tendency is the most significant. The thermal conductivity of Ni3Sn is the highest, the linear expansion coefficients of Ni3Sn and Ni3Sn4 are not significantly different, and they match well with the linear expansion coefficients of Ni.

Semiconductor piezoresistance prediction model for mechanical sensor design

AbstractThis paper addresses semiconductor piezoresistive materials selection in MEMS engineering design. From the practical engineering point of view, it is important to understand piezoresistance properties of semiconductors even if less accuracy under feasibility design phase. However, piezoresistance is frequently analyzed based on first principle electronic band structure simulations by sophisticate physicists. Practical engineers not familiar with this simulation cannot directly apply useful information derived from the result of simulation. This paper provides practical prediction method for piezoresistance based on electronic band parameters obtained from the state‐of‐the‐art solid‐state physics. It is demonstrated that the crucial parameters which control the p‐type shear piezoresistance coefficient π44 in diamond and zinc‐blend single crystals are the inverse of square of bond length in unit cell atom, the square root of valence light hole mass, and the shear elastic compliance coefficient S44.

Evaluation of the electronic properties of layered TlPbI3 perovskites

TlPbI3 is a new three-dimensional layered halide perovskite with special optoelectronic properties. Simulations of unique geometric and electronic structures showed that in contrast to ordinary halide perovskites, the Tl ion orbitals significantly contribute to the bonding interactions. We simulated the low-temperature changes in the TlPbI3 lattice by contracting the lattice constant and found that the bond angles decreased with lattice shrinkage, which reveals a significant lateral displacement between the atoms in the TlPbI3 crystal; this leads to reinforced bonding interactions between Tl, Pb, and I ions. Stronger bonding interactions generate more dispersive band edges, thereby increasing the bandwidth and reducing the bandgap. Moreover, the enhanced dispersion of the band edges resulted in reduced effective carrier masses and anisotropic transport properties. This provides a strategy for modulating the physical performance of new perovskites.

Long-lasting deformation potential effect in Ge induced by UV photoexcitation

Understanding ultrafast response of a semiconductor is necessary for next-generation optoelectronic device applications. Here, we investigate the ultrafast response of an archetypal semiconductor Ge [111] crystal upon photoexcitation at two pump wavelengths, 800 nm (fundamental) and 400 nm (second harmonic, UV radiation), using time-resolved x-ray diffraction (TXRD). The simulated TXRD profiles using a proposed four-layer model and Takagi–Taupin equations reveal that the strain propagation is primarily due to electron diffusion. Intriguingly, the initial (<100 ps) fast decrease in the induced strain for second harmonic excitation suggests that the strain originates from the deformation potential (DP) effect. The higher photon energy (second harmonic pump) excites the electrons to higher conduction band valleys, resulting in DP-induced strain compared to the fundamental pump, which only generates thermo-elastic strain. The DP strain, estimated from the DP coefficient (simulated via density functional theory-based electronic structure simulations) and electron density (simulated from experimental results), shows an excellent agreement with the observed strain. The strain after ∼100 ps delay for second harmonic excitation and strain from fundamental excitation are attributed to the thermo-elastic effect as confirmed by the density-dependent two-temperature model. Our study reveals the long-lasting DP effect in Ge [111], which provides an opportunity to avoid heating in optoelectronic devices due to the thermo-elastic effect at the initial time scale.

Semiconductor Piezoresistance Prediction Model for Mechanical Sensor Design

This paper addresses semiconductor piezoresistive materials selection in MEMS engineering design. From the practical engineering point of view, it is important to understand piezoresistance properties of semiconductors even if less accuracy under feasibility design phase. However, piezoresistance is frequently analyzed based on first principle electronic band structure simulations by sophisticate physicists. Practical engineers not familiar with this simulation cannot directly apply useful information derived from the result of simulation. This paper provides practical prediction method for piezoresistance based on electronic band parameters obtained from the state-of-the-art solid-state physics. It is demonstrated that the crucial parameters which control the p-type shear piezoresistance coefficient π44 in diamond and zinc-blend single crystals are the inverse of square of bond length in unit cell atom, the square root of valence light hole mass, and the shear elastic compliance coefficient S44.

Solution Combustion Synthesis and Characterization of Magnesium Copper Vanadates.

Magnesium vanadate (MgV2O6) and its alloys with copper vanadate were synthesized via the solution combustion technique. Phase purity and solid solution formation were confirmed by a variety of experimental techniques, supported by electronic structure simulations based on density functional theory (DFT). Powder X-ray diffraction combined with Rietveld refinement, laser Raman spectroscopy, diffuse reflectance spectroscopy, and high-resolution transmission electron microscopy showed single-phase alloy formation despite the MgV2O6 and CuV2O6 end members exhibiting monoclinic and triclinic crystal systems, respectively. DFT-calculated optical band gaps showed close agreement in the computed optical bandgaps with experimentally derived values. Surface photovoltage spectroscopy, ambient-pressure photoemission spectroscopy, and Kelvin probe contact potential difference (work function) measurements confirmed a systematic variation in the optical bandgap modification and band alignment as a function of stoichiometry in the alloy composition. These data indicated n-type semiconductor behavior for all the samples which was confirmed by photoelectrochemical measurements.

From tetraalkylphosphonium ionic liquids to phosphonium ylides: How the ionic sizes influence carbon dioxide capture?

Carbon dioxide (CO2) capture via the phosphonium ylide mechanism represents an interesting alternative to alkylamine-based carboxamidation. The tertiary cation-based CO2 sorption supplements the nitrogen-containing anion-based CO2 sorption which ultimately results in very competitive aggregate capacities of such ionic compounds. In the present work, we reveal the impact of the cation and anion sizes on the deprotonation and subsequent carboxylation of the α-methylene carbon of the phosphonium-based cations. In particular, we computationally investigate the role of the alkyl chains and rationalize the previously published experimental results. Since the anions strongly influence the kinetics and thermodynamics of the processes involving their counterions, we herein supplemented the triethyl-butyl phosphonium [P2224] and trihexyl-tetradecyl phosphonium [P66614] cations in our models with the benzimidazolide [BENZIM], and 2–methylthio-benzimidazolide aprotic anions [2MTBENZIM]. By having determined the energetic effects, activation barriers, and steric hindrances faced by the above-referenced processes through potential energy surface scans empowered by electronic-structure simulations, we formulated lucid guidelines for the molecular design of the CO2 scavengers of this class. We found that the alkyl chain length plays a marginal role in the reaction profiles for CO2 capture. In turn, an identity of the anion was found to impact the phosphonium ylide formation stages by 20 % by adjusting local intermolecular interactions. By analyzing molecular geometries in their initial, transition, and product states along with the atomic hydrophobicities, we explained the stepwise energetics of the CO2 chemisorption by the phosphonium-based sorbents of complicated structure and two-stage performance. As we did not detect differences of commensurate magnitude between the [P2224] and [P66614] cations paired with both [BENZIM] and [2MTBENZIM], we linked a somewhat deteriorated experimentally detected performance (∼10 %) of [P66614] to differing shear viscosities and, thus, slowed down the diffusion of CO2. For the sake of practical applications, the [P2224]-based ionic liquids look preferable as long as their physicochemical properties meet the criteria of a specific technological task and the costs of their synthesis are comparable to those of the [P66614]-based ones.

Challenges in the Use of Quantum Computing Hardware-Efficient Ansätze in Electronic Structure Theory.

Advances in quantum computation for electronic structure, and particularly heuristic quantum algorithms, create an ongoing need to characterize the performance and limitations of these methods. Here we discuss some potential pitfalls connected with the use of hardware-efficient Ansätze in variational quantum simulations of electronic structure. We illustrate that hardware-efficient Ansätze may break Hamiltonian symmetries and yield nondifferentiable potential energy curves, in addition to the well-known difficulty of optimizing variational parameters. We discuss the interplay between these limitations by carrying out a comparative analysis of hardware-efficient Ansätze versus unitary coupled cluster and full configuration interaction, and of second- and first-quantization strategies to encode Fermionic degrees of freedom to qubits. Our analysis should be useful in understanding potential limitations and in identifying possible areas of improvement in hardware-efficient Ansätze.

Improving strong scalability of electronic structure simulations with reduced overhead of communications

Physically realizable semiconductor electronic devices easily include more than several million atoms even though they are in nanoscale, so electronic structure simulations of huge atomic systems are the key to predict performance characteristics of such devices. The empirical tight-binding (TB) model, which uses a finite set of localized bases for descriptions of a single atom with assumption of nearest-neighbor coupling, has been extensively adopted to solve electronic structures of large-scale atomic systems, and the size-capability of TB simulations has been continuously improved with aids of parallel computing. Recently, we have demonstrated a strong scalability of TB simulations up to 2,500 computing nodes for a model problem consisting of 400 million atoms, where the scalability is limited due to the overhead of collective communications that the core numerical method must involve to get a partial eigenspectrum of the sparse Hamiltonian system matrix. To reduce the cost of Message Passing Interface (MPI) communications and its effect on the end-to-end simulation time, here we design an equivalent transformation of the sparse matrix eigensolver, and employ a core dedicated to MPI communications with dynamic scheduling of threads. Through rigorous benchmark tests against model problems of various sizes, it is confirmed that our method improves the strong scalability of TB simulations by more than 10% in a huge computing environment, by reducing the time-cost of both core computations and collective communications.

Cost-Effective Simulations of Vibrationally-Resolved Absorption Spectra of Fluorophores with Machine-Learning-Based Inhomogeneous Broadening.

The results of electronic and vibrational structure simulations are an invaluable support for interpreting experimental absorption/emission spectra, which stimulates the development of reliable and cost-effective computational protocols. In this work, we contribute to these efforts and propose an efficient first-principle protocol for simulating vibrationally-resolved absorption spectra, including nonempirical estimations of the inhomogeneous broadening. To this end, we analyze three key aspects: (i) a metric-based selection of density functional approximation (DFA) so to benefit from the computational efficiency of time-dependent density function theory (TD-DFT) while safeguarding the accuracy of the vibrationally-resolved spectra, (ii) an assessment of two vibrational structure schemes (vertical gradient and adiabatic Hessian) to compute the Franck-Condon factors, and (iii) the use of machine learning to speed up nonempirical estimations of the inhomogeneous broadening. In more detail, we predict the absorption band shapes for a set of 20 medium-sized fluorescent dyes, focusing on the bright ππ★ S0 → S1 transition and using experimental results as references. We demonstrate that, for the studied 20-dye set which includes structures with large structural variability, the preselection of DFAs based on an easily accessible metric ensures accurate band shapes with respect to the reference approach and that range-separated functionals show the best performance when combined with the vertical gradient model. As far as band widths are concerned, we propose a new machine-learning-based approach for determining the inhomogeneous broadening induced by the solvent microenvironment. This approach is shown to be very robust offering inhomogeneous broadenings with errors as small as 2 cm-1 with respect to genuine electronic-structure calculations, with a total CPU time reduced by 98%.

Toward Chemical Accuracy Using the Jastrow Correlated Antisymmetrized Geminal Power Ansatz.

Herein, we report accurate atomization energy calculations for 55 molecules in the Gaussian-2 (G2) set using lattice regularized diffusion Monte Carlo (LRDMC). We compare the Jastrow-Slater determinant ansatz with a more flexible JsAGPs (Jastrow correlated antisymmetrized geminal power with singlet correlation) ansatz. AGPs is built from pairing functions, which explicitly include pairwise correlations among electrons, and hence, this ansatz is expected to be more efficient in recovering the correlation energy. The AGPs wave functions are first optimized at the variational Monte Carlo (VMC) level, which includes both the Jastrow factor and the nodal surface optimization. This is followed by the LRDMC projection of the ansatz. Remarkably, for many molecules, the LRDMC atomization energies obtained using the JsAGPs ansatz reach chemical accuracy (∼1 kcal/mol), and for most other molecules, the atomization energies are accurate within ∼5 kcal/mol. We obtained a mean absolute deviation of 1.6 kcal/mol with JsAGPs and 3.2 kcal/mol with JDFT (Jastrow factor + Slater determinant with DFT orbitals) ansatzes. This work shows the effectiveness of the flexible AGPs ansatz for atomization energy calculations and electronic structure simulations in general.