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Monolayer NiI$_2$ garners large research interest due to its multiferroic behavior stemming from the interplay between its non-collinear magnetic order and the spin-orbit coupling. This prompts an investigation into the stability of the magnetic order in NiI$_2$ and similar materials under external stimuli. In this work, we report the effect of biaxial and uniaxial strain on the magnetic ground state, the critical temperature, and the magnonic properties of the NiX$_2$ (X = I, Br, Cl) monolayers. For all three materials, we reveal intricate strain-dependent phase diagrams, including ferromagnetic, helimagnetic, and skyrmionic phases. Moreover, we discuss the necessity of considering the biquadratic exchange interaction in the latter analysis. We reveal that the biquadratic exchange significantly alters both the magnetic ground state and the critical temperature of the magnetic order, and we demonstrate that its importance becomes even more explicit when monolayer Ni-dihalides are strained. Finally, we calculate the magnonic dispersion for the predicted magnetic states, showing that the skyrmionic phase functions as a magnonic crystal, and demonstrate the presence of strain-tunable soft magnon modes at finite wavevectors in the helimagnetic phase.

In the pursuit of advancing solar energy technologies, this study presents 20 direct and quasi-direct band gap silicon crystalline semiconductors that satisfy the Shockley-Queisser limit, a benchmark for solar cell efficiency. Employing two evolutionary algorithm-based searches, we optimize structures and calculate fitness function using the DFTB method and Gaussian approximation potential. Following the preselection of structures based on energy considerations, we further optimize them using PBEsol DFT. Subsequently, we screen the structures based on their band gap, employing a DFTB method tailored for band gap calculation of silicon crystals. To ensure accurate band gap determination, we employ HSE and GW methods. To validate the structural stability, we employ phonon analysis via linear regression algorithm applied to PBEsol DFT data. Significantly, the structures unveiled in this study are of great importance due to their proven stability from both mechanical and dynamic perspectives. Furthermore, the ductility and low density of certain structures enhance their potential application. We examine the optical properties by studying the imaginary part of the dielectric function by solving the Bethe-Salpeter Equation on top of GW approximation. By calculating the SLME, we achieve an efficiency of 32.7% for Si$_{22}$ at a thickness of 500 nm. Moreover, the study harnesses various machine learning algorithms to develop a predictive model for the band gap energy of these silicon structures. Input data for machine learning models are derived from structural MBTR and SOAP descriptors, as well as DFT outputs. Notably, the results reveal that features extracted from DFT outperform the MBTR and SOAP descriptors., Comment: 14 pages, 8 figures, 5 tables

We use density functional theory to investigate effects of spin-orbit coupling and single-stripe-type antiferromagnetic (sAFM) ordering on the crystal structure and electronic properties of SmFeAsO. The results indicate that AFM ordering causes the crystal structure transition from tetragonal to orthorhombic, along with increase in the height of As atoms from Fe layer due to magnetostriction. It also leads to the opening of partial band gaps, the emergence of a prominent peak near Fermi energy (EF) in the density of states (DOS) and a reduction in Fermi surface nesting. The study finds a correlation between the calculated area under the DOS curve, spanning from EF to the first peak above it, and the optimal electron-doped concentration required for inducing superconductivity in SmFeAsO. The findings suggest that spin and charge density waves can play important roles in the superconducting mechanism of Fe-based superconductors. Our calculations demonstrate that spin-orbit coupling reduces electronic correlation., Comment: 29 pages, 8 figures, 1 table

Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approaches have emerged as a promising tool for predicting such properties as transition temperature (Tc) to enable the rapid screening of large databases to discover new superconducting materials. This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials., Comment: 21 pages, 8 figures, 6 Tables

This study investigates the predictive capabilities of common DFT methods (GGA, GGA+$U$, and GGA+$U$+$V$) for determining the transition temperature of antiferromagnetic insulators. We utilize a dataset of 29 compounds and derive Heisenberg exchanges based on DFT total energies of different magnetic configurations. To obtain exchange parameters within a supercell, we have devised an innovative method that utilizes null space analysis to identify and address the limitations imposed by the supercell on these exchange parameters. With obtained exchanges, we construct Heisenberg Hamiltonian to compute Transition temperatures using classical Monte Carlo simulations. To refine the calculations, we apply linear response theory to compute on-site ($U$) and intersite ($V$) corrections through a self-consistent process. Our findings reveal that GGA significantly overestimates the transition temperature (by ~113%), while GGA+$U$ underestimates it (by ~53%). To improve GGA+$U$ results, we propose adjusting the DFT results with the $(S+1)/S$ coefficient to compensate for quantum effects in Monte Carlo simulation, resulting in a reduced error of 44%. Additionally, we discover a high Pearson correlation coefficient of approximately 0.92 between the transition temperatures calculated using the GGA+$U$ method and the experimentally determined transition temperatures. Furthermore, we explore the impact of geometry optimization on a subset of samples. Using consistent structures with GGA+U and GGA+U+V theories reduced the error.

In this study, we use Hubbard-Corrected density functional theory (DFT+$U$) to derive spin model Hamiltonians consisting of Heisenberg exchange interactions up to the fourth nearest neighbors and bi-quadratic interactions. We map the DFT+$U$ results of several magnetic configurations to the Heisenberg spin model Hamiltonian to estimate Heisenberg exchanges. We demonstrate that the number of magnetic configurations should be at least twice the number of exchange parameters to estimate exchange parameters correctly. To calculate biquadratic interaction, we propose specific non-collinear magnetic configurations that do not change the energy of the Heisenberg spin model. We use classical Monte Carlo (MC) simulations to evaluate DFT+$U$ results. We obtain the temperature dependence of magnetic susceptibility and specific heat to determine the Curie-Weiss and N\'eel temperatures. The MC simulations reveal that although the biquadratic interaction can not change the N\'eel temperature, it modifies the order parameter. We indicate that for a fair comparison between classical MC simulations and experiments, we need to consider the quantum effect by applying $(S+1)/S$ correction in classical MC simulations.

Compositional disorder is common in crystal compounds. In these compounds, some atoms are randomly distributed at some crystallographic sites. For such compounds, randomness forms many non-identical independent structures. Thus, calculating the energy of all structures using ordinary quantum ab initio methods can be significantly time-consuming. Machine learning can be a reliable alternative to ab initio methods. We calculate the energy of these compounds with an accuracy close to that of density functional theory calculations in a considerably shorter time using machine learning. In this study, we use kernel ridge regression and neural network to predict energy. In the KRR, we employ sine matrix, Ewald sum matrix, SOAP, ACSF, and MBTR. To implement the neural network, we use two important classes of application of the neural network in material science, including high-dimensional neural network and convolutional neural network based on crystal graph representation. We show that kernel ridge regression using MBTR and neural network using ACSF can provide better accuracy than other methods., Comment: 15pages,3figures,1table

Comprehensive first-principles calculations are performed on diverse arrangements of relevant chemical defects in fluorographene to provide accurate microscopic insights into the process of graphene fluorination. The minimum energy paths for the half- and full-fluorination processes are calculated for a better understanding of these phenomena. While experimental observations indicate a much slower rate of the full-fluorination process, compared with the half-fluorination one, the obtained energy profiles demonstrate much enhanced fluorine adsorption after the half-fluorination stage. This ambiguity is explained in terms of significant chemical activation of the graphene sheet after half-fluorination, which remarkably facilitates the formation of chemical contaminants in the system and thus substantially slows down the full-fluorination procedure. After considering the binding energy and durability of the relevant chemical species, including hydrogen, oxygen, and nitrogen molecules and xenon atom, it is argued that oxygen-fluorine ligands are the most likely chemical contaminants opposing the full-fluorination of a graphene sheet. We propose an oxygen desorption mechanism for the atomic description of the full-fluorination procedure in realistic situations. It is argued that the proposed mechanism explains well much enhanced rate of the full-fluorination procedure at elevated temperatures., Comment: 7 pages, 5 figures, 1 table

The magnetic and structural properties of polycrystalline Co$_{4-x}$ Ni$_x$ Nb$_2$ O$_9$ (x=1,2) have been investigated by neutron powder diffraction, magnetization and heat capacity measurements, and density functional theory (DFT) calculations. For x=1, the compound crystallizes in the trigonal P$\bar{3}$c1 space group. Below T$_N$ = 31 K it develops a weakly non-collinear antiferromagnetig structure with magnetic moments in the ab-plane. The compound with x=2 has crystal structure of the orthorhombic Pbcn space group and shows a hard ferrimagnetic behavior below T$_C$ =47 K. For this compound a weakly non-collinear ferrimagnetic structure with two possible configurations in ab plane was derived from ND study. By calculating magnetic anisotropy energy via DFT, the ground-state magnetic configuration was determined for this compound. The heat capacity study in magnetic fields up to 140 kOe provide further information on the magnetic structure of the compounds., Comment: Published in Physical Review B, 8 pages, 8 figures

There are two discrepant experimental results on the magnetic ground state of Y$_{2}$Mn$_{2}$O$_{7}$, one study proposes a spin glass state, while another introduces the material as a ferromagnet. In this study, we attempt to resolve this issue by employing density functional theory and Monte Carlo simulations. We derive different spin models by varying the Hubbard $U$ parameter in ab initio GGA+$U$ calculations. For the most range of Hubbard $U$, We obtain that the leading terms in the spin Hamiltonian are bi-quadratic and the nearest neighbor Heisenberg exchange interactions. By comparing Monte Carlo simulations of these models with the experiments, we find a ferromagnetic ground state for Y$_{2}$Mn$_{2}$O$_{7}$ as the most compatible with experiments. We also consider Y$_{2}$Mo$_{2}$O$_{7}$ as a prototype of the defect-free pyrochlore system with spin-glass behavior and compare it with Y$_{2}$Mn$_{2}$O$_{7}$. The orbital degrees of freedom are considered as a leading factor in converting a defect-free pyrochlore such as Y$_{2}$Mn$_{2}$O$_{7}$ to a spin glass system. By changing the $d$ orbital occupations of Mo atoms, our GGA+$U$ calculations for Y$_{2}$Mo$_{2}$O$_{7}$ indicate many nearly degenerate states with different $d$ orbital orientations which reveals $d$ orbital degrees of freedom in this material. While for Y$_{2}$Mn$_{2}$O$_{7}$, we find a single ground state with a fixed orbital orientation. Consequently, all of our ab initio approaches confirm Y$_{2}$Mn$_{2}$O$_{7}$ as a ferromagnetic system.

We present \texttt{ESpinS} (Esfahan Spin Simulation) package to evaluate the thermodynamic properties of spin systems described by a spin model Hamiltonian. In addition to the Heisenberg exchange term, the spin Hamiltonian can contain interactions such as bi-quadratic, Dzyaloshinskii-Moriya, and single-ion anisotropy. By applying the classical Monte-Carlo simulation, \texttt{ESpinS} simulates the behavior of spin systems versus temperature. \texttt{ESpinS} ables to calculate the specific heat, susceptibility, staggered magnetization, energy histogram, fourth-order Binder cumulants, and the neutron scattering structure factor. Further, it can compute the user-defined magnetic order parameter i.e. summation of projection of spins on the user-defined directions and the physical quantities based on it. \texttt{ESpinS} works by either local update algorithm or parallel tempering algorithm. The latter feature is an appropriate option for considering the frustrated and spin glass magnetic systems. \texttt{ESpinS} is written in Fortran 90 and can be run in single or parallel mode. The package is freely available under the GPL license (see https://github.com/nafiserb/ESpinS ).

In this work, density functional computations and Monte Carlo simulations are performed to investigate structural, electronic, magnetic, and thermodynamic properties of Co/WS$_2$ junction, a semiconductor WS$_2$ monolayer covered by a ferromagnetic cobalt monolayer. In addition to a conventional semilocal exchange-correlation functional, three nonlocal functional, including the novel ACBN0 scheme, are applied to obtain reliable electronic and magnetic properties. It is argued that the ACBN0 scheme, is very efficient for first principles description of the Co/WS$_2$ junction. The obtained electronic structures evidence a trustworthy half-metallic gap in the majority spin channel of the lowest energy configuration of the junction, promising for spintronic applications. The obtained magnetic thermodynamics properties from Monte Carlo simulations predict a Curie temperature of about 110\,K, which is far small for device applications of this junction. The electronic and magnetic properties of the system are calculated under various compressive and tensile strains and it is shown that a tensile strain of about 4\% may effectively improve thermal stability of half-metallic ferromagnetism in the Co/WS$_2$ junction., Comment: 8 pages, 7 figures, 2 tables

Pyrochlore FeF$_{3}$ (pyr-FeF$_{3}$) is a Heisenberg anti-ferromagnetic (AF) with a magnetic susceptibility deviating from the Curie-Weiss law, even at the room temperature. This compound shows a transition to a long-range ordered state with all-in all-out (AIAO) spin configuration. The critical properties of this transition have remained a matter of dispute. In this work, to gain more insight into the critical properties of pyr-FeF$_{3}$, using ab initio density functional theory (DFT), we obtain spin Hamiltonian of this material under the relative volume change with respect to the experimental volume ($\frac{\Delta V}{V_0}$) from $-0.2$ to $0.2$. We show that the relevant terms in the spin Hamiltonians are the AF exchange up to third neighbors, the nearest neighbor bi-quadratic and the direct Dyzaloshinski-Moriya (DM) interactions and find how these coupling constants vary under the volume change. Then we study the effect of volume change on the finite temperature critical behavior, using classical Monte Carlo (MC) simulation. We show that the spin system undergoes a weakly first order transition to AIAO at small volumes which turns to a second order transition close to the experimental structure. However, increasing $\frac{\Delta V}{V_0}$ to $\sim0.2$, systems shows a transition to a modular spin structure. This finding suggests the existence of a Lifshitz point in pyr-FeF$_{3}$ and may explain the unusual critical exponents observed for this compound., Comment: 8 pages, 14 figures

{\it Ab initio} calculations, GGA/GGA+$U$, are used to propose a spin Hamiltonian for the B-site ordered double perovskite, Sr$_{2}$NiWO$_{6}$. Our results show that the exchange interaction constants between the next nearest neighbors in both intra- and inter- $ab$ plane ($J_2$ and $J_{2c}$) are an order of magnitude larger than the ones between the nearest neighbors ($J_1$ and $J_{1c}$). Employing the Monte Carlo simulation, we show that the obtained Hamiltonian properly describes the finite temperature properties of Sr$_{2}$NiWO$_{6}$. Our {\it ab initio} calculations also reveal a small magnetic anisotropy and non-trivial bi-quadratic interaction between the nearest inter-$ab$ plane neighbors, which play essential roles in stabilizing the type-II anti-ferromagnetic ground state of Sr$_{2}$NiWO$_{6}$., Comment: 8 pages, 6 figures, 1 table

Bi$_3$Mn$_4$O$_{12}$(NO$_3$) (BMNO) is a honeycomb bilayers anti-ferromagnet, not showing any ordering down to very low temperatures despite having a relatively large Curie-Weiss temperature. Using ab initio density functional theory, we extract an effective spin Hamiltonian for this compound. The proposed spin Hamiltonian consists of anti-ferrimagnetic Heisenberg terms with coupling constants ranging up to third intra-layer and fourth inter-layer neighbors. Performing Monte Carlo simulation, we obtain the temperature dependence of magnetic susceptibility and so the Curie-Weiss temperature and find the coupling constants which best matches with the experimental value. We discover that depending on the strength of the interlayer exchange couplings, two collinear spin configurations compete with each other in this system. Both states have in plane N{\'e}el character, however, at small interlayer coupling spin directions in the two layers are antiparallel (N$_1$ state) and discontinuously transform to parallel (N$_2$ state) by enlarging the interlayer couplings at a first order transition point. Classical Monte Carlo simulation and density matrix renormalization group calculations confirm that exchange couplings obtained for BMNO are in such a way that put this material {at the phase boundary of a first order phase transition}, where the trading between these two collinear spin states prevents it from setting in a magnetically ordered state.

In this paper, we employ evolutionary algorithm along with the full-potential density functional theory (DFT) computations to perform a comprehensive search for the stable structures of stoichiometric (WS2)n nano-clusters (n=1-9), within three different exchange-correlation functionals. Our results suggest that n=3, 5, 8 are possible candidates for the low temperature magic sizes of WS2 nano-clusters while at temperatures above 600 Kelvin, n=5 and 7 exhibit higher relative stability among the studied systems. The electronic properties and energy gap of the lowest energy isomers were computed within several schemes, including semilocal PBE and BLYP functionals, hybrid B3LYP functional, many body based DFT+GW approach, and time dependent DFT calculations. Vibrational spectra of the lowest lying isomers, computed by the force constant method, are used to address IR spectra and thermal free energy of the clusters. Time dependent density functional calculation in real time domain is applied to determine the full absorption spectra and optical gap of the lowest energy isomers of the WS2 nano-clusters., Comment: 8 pages, 7 figures and one table

First-principles calculations are performed to investigate f magnetism in A$_2$Ti$_2$O$_7$ (A=Eu, Gd, Tb, Dy, Ho, Er, Yb) magnetic pyrochlore oxides. The Hubbard U parameter and the relativistic spin orbit correction is applied for more accurate description of the electronic structure of the systems. It is argued that the main obstacle for first-principles study of these systems is the multi-minima solutions of their electronic configuration. Among the studied pyrochlores, Gd$_2$Ti$_2$O$_7$ shows the least multi-minima problem. The crystal electric field theory is applied for phenomenological comparison of the calculated spin and orbital moments with the experimental data.

The size-dependent electronic, structural, magnetic and vibrational properties of small pure cop- per and silver clusters and their alloys with one and two palladium atoms are studied by using full-potential all-electron density functional computations. The stable isomers of these clusters are identified and their theoretical magic numbers are determined via the analysis of the second differ- ence of their minimized energy. We discuss that the doped Pd atoms generally prefer to sit in the high coordination sites of the pure clusters. It is argued that Pd doping influences the structural properties and the two dimensional to three dimensional structural cross over in the small Cu and Ag clusters. The many body based GW correction is applied for more accurate determination of the electron affinity and ionization potential of these systems. Magnetic and vibrational properties of the pure and doped clusters are presented and discussed., Comment: 7pages,5figures

It has been suggested that quantum coherence in the selectivity filter of ion channel may play a key role in fast conduction and selectivity of ions. However, it has not been clearly elucidated yet why classical coherence is not sufficient for this purpose. In this paper, we investigate the classical vibrational coherence between carbonyl groups oscillations in the selectivity filter of KcsA ion channels based on the data obtained from molecular dynamics simulations. Our results show that classical coherence plays no effective role in fast ionic conduction., Comment: 8 pages, 8 figures