arXiv subjects

Artem R. Oganov

Publications and source records attributed to Artem R. Oganov.

At least 19 recordsLinked to original sources

arXiv2026

SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion

Reliable crystal structure prediction (CSP) at finite temperatures, including quantum anharmonic effects, remains challenging but is particularly important for systems containing light atoms such as superconducting hydrides. Here, we integrate machine-learned interatomic potentials (MLIPs) with the stochastic self-consistent harmonic approximation (SSCHA) to enable evolutionary CSP on the quantum anharmonic free-energy landscape. Using LaH10 as a test case, we compare three approaches: (i) active-learning MLIPs (AL-MLIPs) trained on the fly, (ii) universal MLIPs (uMLIPs), and (iii) temperature-dependent effective potentials (TDEPs) trained on SSCHA ensemble data. AL-MLIPs correctly predict the cubic Fm-3m phase but require thermodynamic perturbation theory corrections for consistent results. The foundation uMLIP Mattersim-5m enables SSCHA-based CSP without per-structure training, but fine-tuning is required for higher accuracy. Temperature-dependent effective potentials trained on SSCHA data enable accurate and very efficient CSP for large unit cells on the quantum anharmonic free-energy landscape. We demonstrate that quantum anharmonicity simplifies the free-energy landscape and is essential for the correct energy ranking of LaH10 structures.

cond-mat.mtrl-sci↗

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

High-pressure phase stability and superconductivity in La-Zr-H hydrides

Hydrogen-rich ternary hydrides are promising candidates for high-Tc superconductivity at megabar pressures, yet their chemical space is vast and largely unexplored. Combining evolutionary structure searches with first-principles calculations, we comprehensively investigate the La-Zr-H ternary system in the 150-300 GPa pressure range. Zero-point energy-corrected convex hull analysis identifies multiple stable superconducting phases, including R3m-Zr2H17 at 300 GPa and P6/mmm-LaZr2H24 at 200 GPa, both of which are thermodynamically and dynamically stable and exhibit strong electron-phonon coupling. Solution of the Eliashberg equations predicts high superconducting transition temperatures of Tc = 209 K for R3m-Zr2H17 at 300 GPa and Tc = 202 K for P6/mmm-LaZr2H24 at 200 GPa. In addition to these stable phases, we identify a high-symmetry metastable compound, P6m2-LaZrH18, which lies just 0.027 eV/atom above the convex hull yet remains dynamically stable and exhibits a high predicted Tc of 206 K at 300 GPa. We find that, across all phases, the elevated Tc correlates with the high-symmetry structure with dense hydrogen cages, favorable electron counts per hydrogen, and a large hydrogen-derived density of states at the Fermi level. Finally, a random- forest machine learning model, trained on diverse hydrides superconductivity data, reproduces these structure-property trends across predicted structures, enabling to identify potential hydrides with high predicted Tc for targeted follow-up calculations and future high-pressure experiments.

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 Néel-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

Towards superior van der Waals density functionals for molecular crystals

Ubiquitous van der Waals (vdW) interactions play a subtle yet crucial role in determining the precise atomic arrangements in solids, particularly in molecular crystals where these weak forces are the primary link between constituent building blocks. Within density functional (DF) theory, the most natural approach for addressing vdW forces is the use of vdW-inclusive density functionals. Through a detailed analysis of the underlying formalism, we have developed a computational scheme that combines vdW functionals of type DF1 and DF2 and serves as a well optimizable tool to improve the theoretical description and prediction of molecular crystals and other sparse materials. The proof of principle is demonstrated by our consideration of the molecular crystals from the X23 dataset.

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

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

Novel Strontium Carbides Under Compression

Exploring the chemistry of materials at high pressures has lead to the discovery of previously unknown exotic compounds. Here, we systematically search for all thermodynamically stable Sr-C compounds under pressure (up to 100 GPa) using the ab initio evolutionary crystal structure prediction method. Our search lead to the discovery of hitherto unknown phases of SrC3, Sr2C5, Sr2C3, Sr2C, Sr3C2, and SrC. The newly discovered crystal structures feature a variety of different carbon environments ranging from isolated C anions and C-dimers to exotic polyatomic carbon anions including chains, stripes, and infinite ribbons consisting of pentagonal C5 and hexagonal C6 rings. Dynamical stability of all predicted compounds is confirmed by phonons calculations. Bader analysis unravels very diverse chemistry in these compounds and bonding patterns in some of them can be described using Zintl-Klemm rule.

cond-mat.mtrl-sci↗

arXiv2025

Graph Neural Network Prediction of Nonlinear Optical Properties

Nonlinear optical (NLO) materials for generating lasers via second harmonic generation (SHG) are highly sought in today's technology. However, discovering novel materials with considerable SHG is challenging due to the time-consuming and costly nature of both experimental methods and first-principles calculations. In this study, we present a deep learning approach using the Atomistic Line Graph Neural Network (ALIGNN) to predict NLO properties. Sourcing data from the Novel Opto-Electronic Materials Discovery (NOEMD) database and using the Kurtz-Perry (KP) coefficient as the key target, we developed a robust model capable of accurately estimating nonlinear optical responses. Our results demonstrate that the model achieves 82.5% accuracy at a tolerated absolute error up to 1 pm/V and relative error not exceeding 0.5. This work highlights the potential of deep learning in accelerating the discovery and design of advanced optical materials with desired properties.

cond-mat.mtrl-sci↗

arXiv2025

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

The origin of the $A$-type antiferromagnetic ordering, characterized by ferromagnetic layers coupling antiferromagnetically, in the prototype semiconductor altermagnet $α$-MnTe has been a topic of ongoing debate. Experimentally, $α$-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{é}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↗

arXiv2025

Acceleration of crystal structure relaxation with Deep Reinforcement Learning

We introduce a Deep Reinforcement Learning (DRL) model for the structure relaxation of crystal materials and compare different types of neural network architectures and reinforcement learning algorithms for this purpose. Experiments are conducted on Al-Fe structures, with potential energy surfaces generated using EAM potentials. We examine the influence of parameter settings on model performance and benchmark the best-performing models against classical optimization algorithms. Additionally, the model's capacity to generalize learned interaction patterns from smaller atomic systems to more complex systems is assessed. The results demonstrate the potential of DRL models to enhance the efficiency of structure relaxation compared to classical optimizers.

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 Néel 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

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

Shotgun crystal structure prediction using machine-learned formation energies

Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is computationally less intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures.

physics.comp-ph↗

arXiv2024

Substrate-aware computational design of two-dimensional materials

Two-dimensional materials have attracted considerable attention due to their remarkable electronic, mechanical and optical properties, making them prime candidates for next-generation electronic and optoelectronic applications. Despite their widespread use in combination with substrates in practical applications, including the fabrication process and final device assembly, computational studies often neglect the effects of substrate interactions for simplicity. This study presents a novel method for predicting the atomic structure of 2D materials on arbitrary substrates by combining an evolutionary algorithm, a lattice-matching technique, an automated machine learning interatomic potentials training protocol, and the ab initio thermodynamics approach for predicting the possible conditions of experimental synthesis of the predicted 2D structures. Using the Mo-S system on a c-cut sapphire substrate as a case study, we reveal several new stable and metastable structures, including previously known 1H-MoS2 and newly found Pmma Mo3S2, P-1 Mo2S, P21m Mo5S3, and P4mm Mo4S, where the Mo4S structure is specifically stabilized by interaction with the substrate. Electronic band structure calculations of Mo3S2, Mo2S, Mo5S3, and Mo4S showed their metallic behavior, while phonon properties calculations indicated their dynamic stability and substrate-induced modulation of the phonon density of states. Finally, we use the ab initio thermodynamics approach to predict the synthesis conditions of the discovered structures in the parameter space of the commonly used CVD technique. These results provide insights into computational substrate engineering, allowing one to study the substrate effect on the thermodynamic and dynamical stability of 2D materials and to modulate their electronic and phonon properties for their future applications, as well as to provide guide maps for their experimental synthesis.

cond-mat.mtrl-sci↗

arXiv2024

Combining Machine Learning Models with First-Principles High-Throughput Calculation to Accelerate the Search of Promising Thermoelectric Materials

Thermoelectric materials can achieve direct energy conversion between electricity and heat, thus can be applied to waste heat harvesting and solid-state cooling. The discovery of new thermoelectric materials is mainly based on experiments and first-principles calculations. However, these methods are usually expensive and time-consuming. Recently, the prediction of properties via machine learning has emerged as a popular method in materials science. Herein, we firstly did first-principles high-throughput calculations for a large number of chalcogenides and built a thermoelectric database containing 796 compounds. Many novel and promising thermoelectric materials were discovered. Then, we trained four ensemble learning models and two deep learning models to distinguish the promising thermoelectric materials from the others for n type and p type doping, respectively. All the presented models achieve classification accuracy higher than 85% and area under the curve (AUC) higher than 0.9. Especially, the M3GNet model for n type data achieve accuracy, precision and recall all higher than 90%. Our works demonstrate a very efficient way of combining machine learning prediction and first-principles high-throughput calculations together to accelerate the discovery of advanced thermoelectric materials.

cond-mat.mtrl-sci↗

arXiv2024

Raisins in a Hydrogen Pie: Ultrastable Cesium and Rubidium Polyhydrides

We proposed a new method for synthesis of metal polyhydrides via high-pressure thermal decomposition of corresponding amidoboranes in diamond anvil cells. Within this approach, we synthesized molecular semiconducting cesium ($\textit P4/nmm$-CsH$_7$, $\textit P$1-CsH$_{15+x}$) and rubidium (RbH$_{9-x}$) polyhydrides with a very high hydrogen content reaching 93 at%. Preservation of CsH$_7$ at near ambient conditions, confirmed both experimentally and theoretically, represents a significant advance in the stabilization of hydrogen-rich compounds. In addition, we synthesized two crystal modifications of RbH$_{9-x}$ with pseudo hexagonal and pseudo tetragonal structures identified by synchrotron X-ray diffraction and Raman measurements. Both phases are stable at 8-10 GPa. This is an unprecedented low stabilization pressure for polyhydrides. These discoveries open up possibilities for modifying existing hydrogen storage materials to increase their efficiency.

cond-mat.mtrl-sci↗