Pure Density Functional Theory Research Articles

N2O ADSORPTION OVER FLAT AND DEFECTIVE Cu (110) SURFACES: PURE DFT AND DFT+U STUDIES INCLUDING vdW FORCES

The adsorption of N2O on flat and defective Cu (110) surfaces has been studied using pure density functional theory (DFT) and DFT with Hubbard U, including van der Waals (vdW) corrections of the form vdW-DF. The calculations show that for GGA-PBE, as the exchange-correlation and vdW-DF functionals are considered, N2O molecule interacts very weakly with the flat Cu (110) surface, while the presence of the Cu-adatom results in better stability of adsorbed N2O compared to one on the perfect surface. Also, the inclusion of U parameter to PBE and vdW-DF functionals reduces the values of the adsorption energy relative to the standard PBE and vdW-DF functionals with or without spin-polarization. Finally, the analysis of the projected density of states shows a strong hybridization between the Cu-3d band and N-2p orbital. Our investigation suggests that the Cu (110) surface can serve as a catalytic medium that is able to adsorb the harmful N2O molecule.

Accurate state energetics in spin-crossover systems using pure density functional theory.

The energy difference between different spin states of systems with transition metals is an outstanding challenge for electronic structure calculation methods. The small energy difference between high- and low-spin states in spin-crossover systems makes most post-Hartree-Fock or density functional theory-based methods provide inaccurate values. A test case of twenty systems showing spin transitions has been used to evaluate the accuracy of a new family of training meta-GGA (Generalized Gradient Approximation) functionals. One of the functionals of this new family provides comparable or even better values than the best functional reported so far for this type of system, the TPSSh hybrid meta-GGA functional, but without having to use the exact exchange term. It also improves the results obtained with the r2SCAN meta-GGA functional, which was the best alternative to the TPSSh hybrid functional. This makes it possible to calculate the spin energetics of any kind of compound, especially large systems or periodic structures where the exact exchange requires large computational resources.

Exploring electronic properties of zinc oxide using pure and hybrid density functional theory and Compton profiles

To check the viability and applicability of available experimental Compton profiles (CPs) of ZnO, we have computed Mulliken population (MP) and CPs using pure and hybrid density functional theory (DFT) within the basis of linear combination of atomic orbitals (LCAO) scheme. Pure DFT have been employed within local density approximation, generalized gradient approximation (GGA) and second order GGA (SOGGA) whereas for hybrid exchange and correlation energies the standard WC1LYP and B1WC hybrid schemes are adopted. A quantitative comparison of theoretical and the available experimental CPs unambiguously identify WC1LYP as a better choice for reproducing experimental momentum densities, and thereafter hybrid approach for generating accurate Eigen functions of ZnO. Further, MP analysis reports the dominancy of ionic character in the compounds. We have also scaled the CPs of ZnO and CdO using WC1LYP for equal-valence-electron-densities and concluded about more ionic character in ZnO than that in CdO.

Machine learning interatomic potentials as efficient tools for obtaining reasonable phonon dispersions and accurate thermal conductivity: A case study of typical two-dimensional materials

The accurate description of phonon dispersion of two-dimensional (2D) materials demonstrates significance in many research fields of condensed matter physics. In this paper, we systematically calculate the phonon spectra and transport properties of six representative 2D materials (encompassing single-element and binary compounds with flat, buckled, and puckered backbone geometries) by means of density functional theory (DFT) and two machine learning interatomic potentials [MLIPs, on-the-fly machine learning potential (FMLP), and moment tensor potential (MTP)]. The results show that the acoustic out-of-plane flexural (ZA) dispersion of the 2D materials are always and easily exhibiting non-quadratic dispersion phenomena near the center of the Brillouin zone by using the pure DFT calculation method. This phenomenon contradicts physics and reflects intuitively from the non-zero group velocity at Γ point. However, no matter which MLIP (FMLP/MTP) the calculation is based on, it could solve such behavior perfectly, where the ZA mode conforms to the quadratic dispersion relationship in the long-wavelength limit. Our results further demonstrate that compared to the pure DFT calculation, the FMLP and MTP method could quickly and relatively accurately obtain the lattice thermal conductivities of graphene, silicene, phosphorene, SiC, MoS2, and GeS. The findings presented in this work provide a solution about the pseudophysical phenomenon of ZA dispersions in 2D materials with the pure DFT calculation, which will greatly facilitate research areas such as phonon thermal transport, flexural mechanics, and electron–acoustic coupling.

Elucidating the Mystery of DNA-Templating Effects on a Silver Nanocluster.

This presentation considers the effects that DNA-templating has on the optical properties of a 16-atom silver cluster. To accomplish this, hybrid quantum mechanical and molecular mechanical simulations of a Ag16-DNA complex have been carried out and compared with pure time-dependent density functional theory calculations of two Ag16 clusters in vacuum. The presented results show that the templating DNA polymers both red-shift the one-photon absorption of the silver cluster and increase its intensity. This occurs through a change in cluster shape prompted by the structural constraints of the DNA ligands combined with silver-DNA interactions. The overall charge of the cluster also contributes to the observed optical response, as oxidation of the cluster results in a simultaneous blue-shift of the one-photon absorption and a decrease in intensity. Additionally, the changes in shape and environment also lead to a blue-shift and enhancement of the two-photon absorption.

Electronic properties of EuO and Eu2O3: FP-LAPW and LCAO computations and Compton spectroscopy strategies

Spin projected energy bands and density of states (DOS) have been deduced using full potential linearized augmented plane wave (FP-LAPW) with density functional theory (DFT) to analyze the electronic properties of EuO and Eu2O3. Whereas the Mulliken population (MP) and Compton profiles (CPs) for both the compounds have been computed using linear combination of atomic orbitals (LCAO) with pure and hybrid DFT (WC1LYP) schemes. The MP analysis indicates EuO to be more covalent than Eu2O3. Our first ever CP measurements for Eu2O3 (using 20 Ci 137Cs Compton spectrometer) indicate DFT with generalized gradient approximation and WC1LYP as successful prescriptions for predicting the electronic properties of such rare-Earth oxides. In addition, equally normalized experimental CPs of Eu2O3 are compared with other rare-Earth oxides RE2O3 (RE = Nd, Sm, Er and Yb) to decide an overall trend of localisation of f-electrons. A metallic behaviour of Eu2O3 is observed on the basis of real-space analysis of experimental, LCAO and free electron theory based CPs of Eu2O3.

Copper(II)‐TEMPO Interaction

AbstractCopper(II) complexes with N‐oxyl reactants such as TEMPO can selectively oxidize alcohols to aldehydes or ketones. The proposed copper intermediates of the oxidation reaction were extensively theoretically studied, but they were never experimentally detected. Here, we present an analysis of “frozen” intermediates that contain alcohols without α‐hydrogen atoms, thus preventing oxidation. The copper(II)‐TEMPO complexes with a bipyridine‐type ancillary ligand were isolated by electrospray ionization mass spectrometry and investigated spectroscopically by cryogenic photodissociation spectroscopy. The vibrational characteristics of the complexes suggest that TEMPO retains its unpaired electron even upon coordination with copper(II). In agreement, the electronic photodissociation spectra of the TEMPO‐copper(II) complexes show a characteristic band for gaseous copper(II) complexes. These results contradict interpretations of some previous density functional theory (DFT) analyses of possible reaction intermediates. The experimental data were confronted with theoretical results obtained by pure DFT (OPBE, TPSS) and hybrid DFT (TPSSH, B3LYP) calculations. The methods favor different ground states and capture various aspects of the experimental results demonstrating a multiconfigurational character of the copper(II)‐TEMPO complexes.

Van der Waals interactions regulating the hydration of 2-methacryloyloxyethyl phosphorylcholine, the constructing monomer of biocompatible polymers

Van der Waals (VDW) interactions provide fantastic properties for biological systems that function at room temperature. The VDW interaction, which primarily contributes to weak hydrogen bonding, is expected to play a key role in regulating hydrophobic hydration to express the biologically inert biocompatible function of polymerized MPCs (2-methacryloyloxyethyl phosphorylcholine). This report explores at the molecular level the biologically inert function of polymerized MPCs through an array of vibrational spectroscopic and computational characterization of MPC monomers, as temperature-dependent change of intramolecular weak hydrogen bonding. Synchrotron Fourier transform infrared microspectroscopy and terahertz time-domain spectroscopy were used to investigate temperature-dependent spectral changes in the low frequency vibrations of the MPC over the temperature range from cryogenic to room temperature, and the results were analysed by highly reliable well-established density functional theory (DFT) calculations. Complicated spectral features in the low frequency energy region and the uncertain conformations of the MPC in the amorphous powder state are clearly resolved under a polarizable continuum model and dispersion correction to pure DFT calculations.

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

AbstractSurging interests exist in double‐atom catalysts (DACs), which not only inherit the advantages of single‐atom catalysts (SACs) (e.g., ultimate atomic utilization, high activity, and selectivity) but also overcome the drawbacks of SACs (e.g., low loading and isolated active site). The design of DACs, however, remains cost‐prohibitive for both experimental and computational studies, due to their huge design space. Herein, by means of density functional theory (DFT) and topological information‐based machine‐learning (ML) algorithms, we present a data‐driven high‐throughput design principle to evaluate the stability and activity of 16 767 DACs for oxygen evolution (OER) and oxygen reduction (ORR) reactions. The rational design reveals 511 types of DACs with OER activity superior to IrO2 (110), 855 types of DACs with ORR activity superior to Pt (111), and 248 bifunctional DACs with high catalytic performance for both OER and ORR. An intrinsic descriptor is revealed to correlate the catalytic activity of a DAC with the electronic structures of the DAC and its bonding carbon surface structure. This data‐driven high‐throughput approach not only yields remarkable prediction precision (>0.926 R‐squared) but also enables a notable 144 000‐fold reduction of screening time compared with pure DFT calculations, holding promise to drastically accelerate the design of high‐performance DACs.

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials

With the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10−1 and 103 Wm−1K−1. Among various choices of training sets, we find that molecular dynamics trajectories at 50–700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2–10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ∼50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.

Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts

SummaryThe oxygen evolution reaction (OER) is a critical reaction for energy-related applications, yet suffers from its slow kinetics and large overpotential. It is desirable to develop effective OER electrocatalysts, such as single-atom catalysts (SACs). Here, we demonstrate machine learning (ML)-accelerated prediction of OER overpotential of all transition metals. Based on density functional theory (DFT) calculations of 15 species of SACs, we design a topological information-based ML model to map the OER overpotentials with atomic properties of the corresponding SACs. The trained ML model not only yields remarkable prediction precision (relative error of 6.49%) but also enables a 130,000-fold reduction of prediction time in comparison with pure DFT calculation. Furthermore, an intrinsic descriptor that correlates the overpotential of an SAC with its atomic properties is revealed. The approach and results from this study can be readily applicable to screen other SACs and significantly accelerate the design of high-performance catalysts for many other reactions.

Computational Design of Cathode Coating Materials for All-Solid-State Lithium Ion Batteries with on-the-Fly Machine Learning

All-solid-state lithium-ion batteries have attracted significant research interest in recent years. Solid-state electrolytes have emerged as an appealing alternative to flammable organic liquid electrolytes, with the potential to accommodate higher-voltage cathode materials and a metallic lithium anode. Despite the discoveries of solid electrolyte materials exhibiting superionic conductivities, capacity loss and rate performance degradation has been commonly observed due to the prevailing issue of electrolyte/electrode interface reactivity, which originates from abrupt electrochemical potential changes at the electrode-electrolyte interface. The cycling stability of solid-state lithium ion batteries can be improved through the use of interfacial coating materials, but computationally identifying materials with sufficiently high lithium-ion conductivity can be challenging. Methods such as ab initio molecular dynamics that work well for superionic conductors can be prohibitively expensive when used on materials that conduct lithium ions less well but are still suitable for use as interfacial coatings. We demonstrate a way to address this problem using machine-learned interatomic potentials models in the form of moment tensor potentials. To prevent the potentials from significantly deviating from density functional theory (DFT) calculations we use molecular dynamics simulations coupled with on-the-fly machine learning. The efficiency of the calculations is increased by seven orders of magnitude compared to pure DFT, allowing for significantly improved prediction accuracy relative to experimental benchmarks. Using this approach, we have identified two particularly promising materials, Li3B7O12 and Li3Sc2(PO4)3, that are predicted to enhance long-term cyclability. The top candidate, Li3B7O12, has outstanding interface stability and to our knowledge has not previously been investigated as a lithium-ion conductor.

Efficient and Accurate Approach To Estimate Hybrid Functional and Large Basis-Set Contributions to Condensed-Phase Systems and Molecule-Surface Interactions.

We present a graph theoretic approach to adaptively compute contributions from many-body approximations in an efficient manner and perform accurate hybrid density functional theory (DFT) electronic structure calculations for condensed-phase systems. The salient features of the approach are ONIOM-like. (a) The full-system calculation is performed at a lower level of theory (pure DFT) by enforcing periodic boundary conditions. (b) This treatment is then improved through a correction term that captures many-body interactions up to any given order within a higher (in this case, hybrid DFT) level of theory. (c) In the spirit of ONIOM, contributions from the many-body approximations that arise from the higher level of theory [i.e., from (b) above] are included through extrapolation from the lower level calculation. The approach is implemented in a general, system-independent, fashion using the graph-theoretic functionalities available within Python. For example, the individual one-body components within the unit cell are designated as "nodes" within a graph. The interactions between these nodes are captured through edges, faces, tetrahedrons, and so forth, thus building a many-body interaction hierarchy. Electronic energy extrapolation contributions from all of these geometric entities are included within the above-mentioned ONIOM paradigm. The implementation of the method simultaneously uses multiple electronic structure packages. Here, for example, we present results which use both the Gaussian suite of electronic structure programs and the Quantum ESPRESSO program within a single calculation. Thus, the method integrates both plane-wave basis functions and atom-centered basis functions within a single structure calculation. The method is demonstrated for a range of condensed-phase problems for computing (i) hybrid DFT energies for condensed-phase systems at pure DFT cost and (ii) large, triple-zeta, multiply polarized, and diffuse atom-centered basis-set energies at computational costs commensurate with much smaller sets of basis functions. The methods are demonstrated through calculations performed on (a) homogeneous water surfaces as well as heterogeneous surfaces that contain organic solutes studied using two-dimensional periodic boundary conditions and (b) bulk simulations of water enforced through three-dimensional periodic boundary conditions. A range of structures are considered, and in all cases, the results are in good agreement with those obtained using large atom-centered basis and hybrid DFT calculations on the full periodic systems at significantly lower cost.

Prediction of Li intercalation voltages in rechargeable battery cathode materials: Effects of exchange-correlation functional, van der Waals interactions, and Hubbard U

Quantitative predictions of the Li intercalation voltage and of the electronic properties of rechargeable battery cathode materials are a substantial challenge for first-principles theory due to the possibility of (1) strong correlations associated with localized transition metal $d$ electrons and (2) significant van der Waals (vdW) interactions in layered systems, both of which are not accurately captured by standard approximations to density functional theory (DFT). Here, we perform a systematic benchmark of electronic structure methods based on the widely used generalized-gradient approximation of Perdew, Burke, and Ernzerhof (PBE) and the new strongly constrained and appropriately normed (SCAN) meta-generalized-gradient approximation for battery cathode materials. Studying layered ${\mathrm{Li}}_{x}{\mathrm{TiS}}_{2}, {\mathrm{Li}}_{x}{\mathrm{NiO}}_{2}$, and ${\mathrm{Li}}_{x}{\mathrm{CoO}}_{2}$, olivine ${\mathrm{Li}}_{x}{\mathrm{FePO}}_{4}$, and spinel ${\mathrm{Li}}_{x}{\mathrm{Mn}}_{2}{\mathrm{O}}_{4}$, we compute the voltage, crystal structure, and electronic structure with and without extensions to incorporate onsite Hubbard interactions and vdW interactions. Within pure DFT (i.e., without corrections for onsite Hubbard interactions), SCAN is a significant improvement over PBE for describing cathode materials, decreasing the mean absolute voltage error by more than 50%. Although explicit vdW interactions are not critical and in cases even detrimental when applied in conjunction with SCAN, Hubbard-$U$ corrections are still in general necessary to achieve reasonable agreement with experiment. We show that no single method considered here can accurately describe the voltage and overall structural, electronic, and magnetic properties (i.e., errors no more than 5% for voltage, volume, band gap, and magnetic moments) of battery cathode materials, motivating a strong need for improved electronic structure approaches for such systems.

Embedded, graph‐theoretically defined many‐body approximations for wavefunction‐in‐DFT and DFT‐in‐DFT: Applications to gas‐ and condensed‐phase ab initio molecular dynamics, and potential surfaces for quantum nuclear effects

AbstractWe present a graph‐theoretic approach to adaptively compute many‐body approximations in an efficient manner to perform (a) accurate post‐Hartree–Fock (HF) ab initio molecular dynamics (AIMD) at density functional theory (DFT) cost for medium‐ to large‐sized molecular clusters, (b) hybrid DFT electronic structure calculations for condensed‐phase simulations at the cost of pure density functionals, (c) reduced‐cost on‐the‐fly basis extrapolation for gas‐phase AIMD and condensed phase studies, and (d) accurate post‐HF‐level potential energy surfaces at DFT cost for quantum nuclear effects. The salient features of our approach are ONIOM‐like in that (a) the full system (cluster or condensed phase) calculation is performed at a lower level of theory (pure DFT for condensed phase or hybrid DFT for molecular systems), and (b) this approximation is improved through a correction term that captures all many‐body interactions up to any given order within a higher level of theory (hybrid DFT for condensed phase; CCSD or MP2 for cluster), combined through graph‐theoretic methods. Specifically, a region of chemical interest is coarse‐grained into a set of nodes and these nodes are then connected to form edges based on a given definition of local envelope (or threshold) of interactions. The nodes and edges together define a graph, which forms the basis for developing the many‐body expansion. The methods are demonstrated through (a) ab initio dynamics studies on protonated water clusters and polypeptide fragments, (b) potential energy surface calculations on one‐dimensional water chains such as those found in ion channels, and (c) conformational stabilization and lattice energy studies on homogeneous and heterogeneous surfaces of water with organic adsorbates using two‐dimensional periodic boundary conditions.

High energy γ-ray Compton spectroscopy and electronic response of rare earth sesquioxides Er2O3 and Yb2O3

In this paper, we present Compton profiles (CPs) of sesquioxides Er2O3 and Yb2O3 using 662 keV gamma-rays emitted by 20 Ci 137Cs source. To validate experimental momentum densities, theoretical CPs, energy bands, density of states (DOS), Mulliken's population of the both sesquioxides have been computed using linear combination of atomic orbitals (LCAO) method with pure and hybrid density functional theory (DFT). Present CP measurements confirm the applicability of hybrid (HF+DFT) approximation, namely WC1LYP, for predicting electron momentum densities and thereby electronic properties of such rare earth oxides. Energy bands and DOS unambiguously show insulating nature of the both compounds and the band gap values are found to be in tune with the available data. Further, the nature of bonding in Er2O3 and Yb2O3 is explained interms of the Mulliken's population and equally normalized CPs. Sharpness in Compton lines is also discussed in terms of localization of valence 5d states.

(Invited) Discovery and Design of Alloy Catalysts and Lithium-Ion Battery Materials through Machine Learning

Drawing on examples from alloy catalysis and lithium-ion batteries, I will demonstrate how machine learning can be used to significantly accelerate the design and discovery of new materials. In the first example, I will present a way to create complete maps of the surface structures and catalytic properties of an alloy catalyst at every point in its phase diagram. The key to this approach is the generation of surface cluster expansions that depend on the lattice parameter of the bulk material and are trained using a Bayesian method. I will discuss how these maps reveal insights into the catalytic properties of intermetallic phases and demonstrate how the maps can be used to rapidly identify the target synthesis conditions for stable and active catalysts. In the second example, I will demonstrate how training an interatomic potential model using on-the-fly machine learning can accelerate the calculation of activation energies for lithium diffusion by seven orders of magnitude, resulting in significantly improved agreement with experiment compared to pure density functional theory. Using the machine-learned interatomic potential models, we have identified new candidate coating materials designed to improve interfacial stability in solid state lithium-ion batteries.

Accurate optical spectra of solids from pure time-dependent density functional theory

We present accurate optical spectra of semiconductors and insulators within a pure Kohn-Sham time-dependent density-functional approach. In particular, we show that the onset of the absorption is well reproduced when comparing to experiment. No empirical information nor a theory beyond Kohn-Sham density-functional theory, such as $GW$, is invoked to correct the Kohn-Sham gap. Our approach relies on the link between the exchange-correlation kernel of time-dependent density functional theory and the derivative discontinuity of ground-state density-functional theory. We show explicitly how to relate these two quantities. We illustrate the accuracy and simplicity of our approach by applying it to various semiconductors (Si, GaP, GaAs) and wide-gap insulators (C, LiF, Ar).

What Is the Structure of the E4 Intermediate in Nitrogenase?

Nitrogenase is the only enzyme that can cleave the strong triple bond in N2. The active site contains a complicated MoFe7S9C cluster. It is believed that it needs to accept four protons and electrons, forming the E4 state, before it can bind N2. However, there is no consensus on the atomic structure of the E4 state. Experimental studies indicate that it should contain two hydride ions bridging two pairs of Fe ions, and it has been suggested that both hydride ions as well as the two protons bind on the same face of the cluster. On the other hand, density functional theory (DFT) studies have indicated that it is energetically more favorable with either three hydride ions or with a triply protonated carbide ion, depending on the DFT functional. We have performed a systematic combined quantum mechanical and molecular mechanical (QM/MM) study of possible E4 states with two bridging hydride ions. Our calculations suggest that the most favorable structure has hydride ions bridging the Fe2/6 and Fe3/7 ion pairs. In fact, such structures are 14 kJ/mol more stable than structures with three hydride ions, showing that pure DFT functionals give energetically most favorable structures in agreement with experiments. An important reason for this finding is that we have identified a new type of broken-symmetry state that involves only two Fe ions with minority spin, in contrast to the previously studied states with three Fe ions with minority spin. The energetically best structures have the two hydride ions on different faces of the FeMo cluster, whereas better agreement with ENDOR data is obtained if they are on the same face; such structures are only 6–22 kJ/mol less stable.

Performance of hybrid functional in linear combination of atomic orbitals scheme in predicting electronic response in spinel ferrites ZnFe2O4 and CdFe2O4

Pure and hybrid density functional theory (DFT) schemes within linear combination of atomic orbitals (LCAO) have been employed to compute Mulliken population (MP), energy bands, density of states (DOS) and electron momentum densities (EMDs) of TMFe2O4 (TM = Zn and Cd). Pure DFT calculations were performed within local density and generalized gradient approximations, while Hartree–Fock exchange contribution is added to DFT for hybrid calculations (B3LYP and PBE0). To validate the performance of hybrid functionals, we have also performed EMD measurements using 661.65 keV γ-rays from 137Cs source. Chi-square test predicts an overall better agreement of experimental Compton profile data with LCAO–B3LYP scheme-based momentum densities leading to usefulness of hybrid functionals in predicting electronic and magnetic response of such ferrites. Further, LCAO–B3LYP-based majority- and minority-spin energy bands and DOS for ZnFe2O4 and CdFe2O4 predict semiconducting nature in both the compounds. In addition, MP data and equal-valence-electron-density scaled EMDs show more covalent character of ZnFe2O4 than that of CdFe2O4. A reasonable agreement of magnetic moments of both the ferrites with available data unambiguously promotes use of Gaussian-type orbitals in LCAO scheme in exploring magnetic properties of such ferrites.