arXiv subjects

Mojtaba Alaei

Publications and source records attributed to Mojtaba Alaei.

At least 19 recordsLinked to original sources

arXiv2026

Pressure-Tunable Electronic and Magnonic Transport in Altermagnet La$_2$O$_3$Mn$_2$Se$_2$

Hydrostatic pressure provides a symmetry-preserving route to engineer electronic and magnonic transport in the correlated insulating altermagnet La$_2$O$_3$Mn$_2$Se$_2$. Using first-principles calculations combined with spin-Hamiltonian modeling, we show that compression from 0 to 40 GPa markedly enhances the inequivalence between the competing second-neighbor exchange interactions, increasing $|J_{2a}-J_{2b}|$ from 1.97 to 9.38 meV while preserving the compensated antiferromagnetic ground state. The resulting exchange anisotropy amplifies the momentum-dependent splitting between the two chiral magnon branches, yielding a nearly fourfold enhancement of the longitudinal magnon-driven spin Seebeck response at 100 K, from $3.68\times10^{-1}$ to $1.36$ meV/K. In contrast, hydrostatic pressure preserves the magnetic-symmetry selection rules governing the anomalous Hall effect while redistributing the electronic Berry curvature, producing pronounced energy-dependent sign reversals in the anomalous Hall conductivity. These results identify exchange anisotropy as the microscopic mechanism underlying the pressure-enhanced magnon response and establish hydrostatic pressure as an effective means of simultaneously controlling electronic and magnonic transport in insulating altermagnets.

cond-mat.mtrl-sci↗

arXiv2026

Complex Magnetic Behavior in RuO$_2$ Thin Films Driven by Strain and Substrate Effects

Ruthenium dioxide (RuO$_2$) has been proposed as a prototypical metallic $d$-wave altermagnet, a N\'eel-ordered compensated antiferromagnetic state exhibiting nonrelativistic momentum-dependent spin splitting; yet, its magnetic ground state remains controversial both theoretically and experimentally. Using comprehensive first-principles calculations, we investigate RuO$_2$ thin films with (110), (100), and (001) orientations, both (un)strained freestanding and supported on a TiO$_2$ substrate. We show that emergent magnetic moments in RuO$_2$ thin films are highly fragile, strongly influenced by strain, surface orientation, and atomic relaxation, while also being highly sensitive to the choice of the Brillouin-zone integration scheme. We find that none of the thin film structures considered can stabilize a compensated antiferromagnetic order; therefore, an altermagnetic ground state cannot be realized. Instead, substrate-supported RuO$_2$ films on TiO$_2$ exhibit pronounced layer- and site-dependent magnetic moment variations and incomplete compensation between the two antiferromagnetically coupled Ru moments, yielding a \emph{ferrimagnetic-like} behavior. On the other hand, freestanding RuO$_2$ films display complex magnetic structures depending on their orientation and applied strain, with distinct behavior at the surfaces and in the inner layers. Our results reconcile conflicting theoretical and experimental reports and underscore the sensitivity of RuO$_2$ magnetism to structural and methodological details.

cond-mat.mtrl-sci↗

arXiv2025

Benchmarking First-Principles Approaches for Extracting Magnetic Exchange Interactions

Magnetic exchange interactions govern the macroscopic magnetic behavior of solids and underpin both fundamental spin phenomena and emerging technologies. The accurate and efficient determination of these interactions is therefore critical for predictive modeling of magnetic materials. Here we present a systematic first-principles comparison of three widely used approaches-the Least-Squares Total Energy (LSTE), the Four-State Total Energy (FSTE), and the Green's function-based Liechtenstein \textit{et al.} (LKAG) methods-applied to thirteen antiferromagnetic compounds. We introduce an framework for identifying the minimal supercells required for an accurate exchange parameter extraction in the FSTE method, significantly reducing computational cost while preserving precision. Our results show that LSTE and FSTE yield nearly identical exchange parameters, whereas the LKAG method reproduces the dominant exchange interactions but exhibits quantitative deviations. A detailed analysis of computational efficiency versus accuracy reveals that the LSTE scheme offers the most favorable balance, establishing a general, reproducible, and scalable workflow for Heisenberg mapping, while the FSTE approach remains the most straightforward for extracting specific exchange interactions.

cond-mat.mtrl-sci↗

arXiv2025

Predicting the Curie Temperature of Magnetic Materials with Machine Learning: Descriptor Engineering, Graph Neural Networks, and the Role of Curated Data

Predicting the Curie temperature ($T_\mathrm{C}$) of magnetic materials is crucial for advancing applications in data storage, spintronics, and sensors. We present a machine learning (ML) framework to predict $T_{\mathrm{C}}$ using a curated dataset of 2,500 ferromagnetic compounds, employing two types of elemental descriptor-based features: one based on stoichiometry-weighted descriptors, and the other leveraging Graph Neural Networks (GNNs). CatBoost trained on the stoichiometry-weighted descriptors achieved an $R^2$ score of 0.87, while the use of GNN-based representations led to a further improvement, with CatBoost reaching an $R^2$ of 0.91, highlighting the effectiveness of graph-based feature learning. We also demonstrated that using an uncurated dataset available online leads to poor predictions, resulting in a low $R^2$ score of 0.66 for the CatBoost model. We analyzed feature importance using tools such as Recursive Feature Elimination (RFE), which revealed that ionization energies are a key physicochemical factor influencing $T_\mathrm{C}$. Notably, the use of only the first 10 ionization energies as input features resulted in high predictive accuracy, with $R^2$ scores of up to 0.85 for statistical models and 0.89 for the GNN-based approach. These results highlight that combining robust ML models with thoughtful feature engineering and high-quality data, can accelerate the discovery of magnetic materials. Our curated dataset is publicly available on GitHub.

cond-mat.mtrl-sci↗

arXiv2025

1D Transition Metal Oxide Chains as a Challenging Model for Ab Initio Calculations

Providing highly simplified models of strongly correlated electronic systems that challenge {\it ab initio} calculations can serve as a valuable testing ground to improve these methods. In this study, we present a comprehensive study of the structural, magnetic, and electronic properties of one-dimensional transition metal mono-oxide chains (VO, CrO, MnO, FeO, CoO, and NiO) using density functional theory (DFT), DFT+$U$, and coupled-cluster singles and doubles (CCSD) calculations. The Hubbard $U$ parameter for DFT+$U$ is determined using the linear response theory. In all systems studied except MnO, the presence of multiple local minima -- primarily due to the electronic degrees of freedom associated with the d-orbitals -- leads to significant challenges for DFT, DFT+U, and Hartree-Fock methods in finding the global minimum in ab initio calculations. Our results indicate that the antiferromagnetic (AFM) state is energetically favored for all chains, except CrO, when using DFT+$U$ and PBE. We analyze the electronic band structures and find that while the PBE approximation often predicts metallic or half-metallic ground states for the ferromagnetic (FM) state, DFT+$U$ approach successfully opens band gaps, correctly predicting insulating behavior in all cases. Furthermore, we compared the energy differences between the AFM and FM states using DFT+$U$ and CCSD for CrO, MnO, FeO, CoO and NiO. Our findings indicate that CCSD predicts larger energy differences in some cases compared to DFT+$U$, suggesting that the Hubbard $U$ parameter obtained through linear response theory may be overestimated when used to calculate energy differences between different magnetic states. For CrO, CCSD predicts an AFM ground state, in contrast to the predictions from DFT+$U$ and PBE methods.

cond-mat.str-el↗

arXiv2025

Experimental Exchange Interaction Dataset for Magnetic Materials: Spin Waves to MC Simulations

Inelastic neutron scattering (INS) provides direct insights into microscopic magnetic interactions in crystalline materials, making it a valuable experimental technique in condensed matter physics and materials science. These interactions can be extracted by fitting spin wave dispersions to Heisenberg Hamiltonians using spin wave theory. However, such datasets are scattered across the literature and lack a standardized format, which limits their accessibility, reproducibility, and utility. In this work, we compile and standardize exchange interaction data obtained from INS experiments on nearly 100 magnetic materials. The resulting dataset includes exchange parameters expressed in a unified Heisenberg model format, visualizations of crystal structures with annotated exchange pathways, and Monte Carlo simulation files generated using the ESpinS code. We use these experimentally derived exchange interactions to compute magnetic transition temperatures ($T_c$) via classical Monte Carlo simulations. Furthermore, we examine the impact of the $(S+1)/S$ correction in the simulations and find it improves agreement with experimental $T_c$ values in most cases. All data and related resources are openly available through a public GitHub repository.

cond-mat.mtrl-sci↗

arXiv2025

Evaluating SCAN and r$^2$SCAN meta-GGA functionals for predicting transition temperatures in antiferromagnetic materials

Recent advancements in exchange-correlation functionals within density functional theory highlight the need for rigorous validation across diverse types of materials properties. In this study, we assess the performance of the newly developed meta-GGA r$^2$SCAN and its predecessor, SCAN, in predicting the N\'eel transition temperature of antiferromagnetic materials. Our analysis includes 48 magnetic materials, spanning both simple and complex systems. Using DFT, we compute the energies of various magnetic configurations and extract exchange interaction parameters through a least-squares fitting approach. These parameters are then used in classical Monte Carlo simulations to estimate the transition temperatures. Our results demonstrate that both SCAN and r$^2$SCAN greatly outperform standard GGA and GGA+$U$ methods, yielding predictions that closely align with experimental values. The Pearson correlation coefficients for SCAN and r$^2$SCAN are 0.97 and 0.98, respectively, when compared to experimental transition temperatures. Additionally, we calculate the energy differences between antiferromagnetic and ferromagnetic configurations to assess the performance of the hybrid HSE06 functional. We found that the HSE06 functional underestimates transition temperatures compared to the meta-GGA functionals and experimental values.

cond-mat.mtrl-sci↗

arXiv2024

Origin of $A$-type antiferromagnetism and chiral split magnons in altermagnetic $\alpha$-MnTe

The origin of the $A$-type antiferromagnetic ordering, characterized by ferromagnetic layers coupling antiferromagnetically, in the prototype semiconductor altermagnet $\alpha$-MnTe has been a topic of ongoing debate. Experimentally, $\alpha$-MnTe exhibits an in-plane ferromagnetic exchange interaction, whereas previous \emph{ab initio} calculations predicted an antiferromagnetic interaction. In this paper, we resolve this discrepancy by considering an expanded set of magnetic configurations, which reveals a ferromagnetic in-plane exchange interaction in agreement with experimental findings. Additionally, we demonstrate that the 10th nearest-neighbor exchange interaction is directionally dependent, inducing a nonrelativistic chiral splitting in the magnon bands, as recently observed experimentally. We further show that applying a compressive strain may significantly enhance both nonrelativistic spin and chiral magnon splittings. The strain can also change the sign of the in-plane exchange interaction. Computing magnetic susceptibility, we show that strain enhances the N{\'e}el temperature, significantly. Our results highlight the critical importance of convergence in the number of magnetic configurations for spin interactions in antiferromagnetic materials.

cond-mat.mtrl-sci↗

arXiv2024

Optimizing Supercell Structures for Heisenberg Exchange Interaction Calculations

In this paper, we introduce an efficient, linear algebra-based method for optimizing supercell selection to determine Heisenberg exchange parameters from DFT calculations. A widely used approach for deriving these parameters involves mapping DFT energies from various magnetic configurations within a supercell to the Heisenberg Hamiltonian. However, periodic boundary conditions in crystals limit the number of exchange parameters that can be extracted. To identify supercells that allow for more exchange parameters, we generate all possible supercell sizes within a specified range and apply null space analysis to the coefficient matrix derived from mapping DFT results to the Heisenberg Hamiltonian. By selecting optimal supercells, we significantly reduce computational time and resource consumption. This method, which involves generating and analyzing supercells before performing DFT calculations, has demonstrated a reduction in computational costs by 1-2 orders of magnitude in many cases.

cond-mat.mtrl-sci↗

arXiv2024

Strain-tunable magnetic and magnonic states in Ni-dihalide monolayers

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.

cond-mat.mes-hall↗

arXiv2024

Discovery of Novel Silicon Allotropes with Optimized Band Gaps to Enhance Solar Cell Efficiency through Evolutionary Algorithms and Machine Learning

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.

cond-mat.mtrl-sci↗

arXiv2024

Driven charge density modulation by spin density wave and their coexistence interplay in SmFeAsO: A first-principles study

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.

cond-mat.supr-con↗

arXiv2024

Predicting Superconducting Transition Temperature through Advanced Machine Learning and Innovative Feature Engineering

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.

cond-mat.supr-con↗

arXiv2023

Benchmarking density functional theory on the prediction of antiferromagnetic transition temperatures

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.

physics.comp-ph↗

arXiv2022

Machine Learning for compositional disorder: A Comparison Between Different Descriptors and Machine Learning Frameworks

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.

cond-mat.mtrl-sci↗

arXiv2022

A deep investigation of NiO and MnO through the first principle calculations and Monte Carlo simulations

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.

cond-mat.mtrl-sci↗

arXiv2021

Novel First-Principles Insights into Graphene Fluorination

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.

cond-mat.mes-hall↗

arXiv2021

ESpinS: A program for classical Monte-Carlo simulations of spin systems

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 ).

physics.comp-ph↗