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Although multiferroics have undergone extensive examination for several decades, the occurrence of ferroelectricity induced by orbital order is only scarcely documented. In this study, we propose the existence of spontaneous ferroelectric states featuring a finite out-of-plane polarization in monolayer compounds of magnetic transition metal di-halides. Our first principles analysis reveals that partially occupied d-orbital states within octahedra exhibit a preference for spatial orbital order within a two-dimensional lattice. The absence of inversion symmetry, arising from orbital order, serves as the driving force introducing additional electric polarization along the out-of-plane direction. Unlike previous reported orbital orders arising from metal states in lattice, the non-colinear ones we studied in this work relate to the transition between two insulator states. The resultant asymmetric Jahn-Teller distortions are accompanied as the consequence producing additional ionic polarization. Importantly, our findings indicate that this mechanism is not confined to a specific material but is a shared characteristic among a series of monolayer transition metal magnetic di-halides, proposing an innovative form of intrinsic two-dimensional multiferroic physics.

Accurately calculating energies and atomic forces with linear-scaling methods is a crucial approach to accelerating and improving molecular dynamics simulations. In this paper, we introduce HamGNN-DM, a machine learning model designed to predict atomic forces and energies using local density matrices in molecular dynamics simulations. This approach achieves efficient predictions with a time complexity of O(n), making it highly suitable for large-scale systems. Experiments in different systems demonstrate that HamGNN-DM achieves DFT-level precision in predicting the atomic forces in different system sizes, which is vital for the molecular dynamics. Furthermore, this method provides valuable electronic structure information throughout the dynamics and exhibits robust performance., Comment: 13 pages, 6 figures

Intrinsic breakdown strength (F_bd), as the theoretical upper limit of electric field strength that a material can sustain, plays important roles in determining dielectric and safety performance. The well accepted concept is that a larger band gap (E_g) often leads to a larger intrinsic breakdown strength. In this work, we analytically derive a simplified model of F_bd, showing a linear relationship between F_bd and the maximum electron density of states (DOS_max) within the energy range spanning from the conduction band minimum (CBM) to CBM+E_g. Using the Wannier interpolation technique to reduce the cost of calculating the F_bd for various three- and two-dimensional materials, we find that the calculated F_bd did not show any simple relationship with band gap, but it behaves linearly with the DOS_max, consistent with our theoretical derivation. Our work shows that the DOS_max is more fundamental than the band gap value in determining the F_bd, thus providing useful physical insights into the intrinsic dielectric breakdown strength and opening directions for improving high-power devices. The dimensional effects on F_bd has also been revealed that monolayers tend to have larger F_bd due to reduced screening effects.

The application of machine learning methodologies for predicting properties within materials science has garnered significant attention. Among recent advancements, Kolmogorov-Arnold Networks (KANs) have emerged as a promising alternative to traditional Multi-Layer Perceptrons (MLPs). This study evaluates the impact of substituting MLPs with KANs within three established machine learning frameworks: Allegro, Neural Equivariant Interatomic Potentials (NequIP), and the Edge-Based Tensor Prediction Graph Neural Network (ETGNN). Our results demonstrate that the integration of KANs generally yields enhanced prediction accuracies. Specifically, replacing MLPs with KANs in the output blocks leads to notable improvements in accuracy and, in certain scenarios, also results in reduced training times. Furthermore, employing KANs exclusively in the output block facilitates faster inference and improved computational efficiency relative to utilizing KANs throughout the entire model. The selection of an optimal basis function for KANs is found to be contingent upon the particular problem at hand. Our results demonstrate the strong potential of KANs in enhancing machine learning potentials and material property predictions.

Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N$^2$AMD which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. The preservation of Euclidean symmetry of Hamiltonian enables N$^2$AMD to achieve state-of-the-art performance. Distinct from conventional machine learning methods that predict key quantities in NAMD, N$^2$AMD computes these quantities directly with a deep neural Hamiltonian, ensuring supreme accuracy, efficiency, and consistency. Furthermore, N$^2$AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework not only boosts the efficiency and precision of NAMD simulations but also opens new avenues to advance materials research., Comment: 25 pages, 7 figures

HfO2-based ferroelectric materials are promising for the next generation of memory devices, attracting significant attention. However, their potential applications are significantly limited by fatigue and imprint phenomena, which affect device lifetime and memory capabilities. Here, to accurately describe the dynamics and field effects of HfO2, we adopt our newly developed DREAM-Allegro network scheme and develop a comprehensive machine-learning model for HfO2. Such model can not only predict the interatomic potential, but also predict Born effective charges. Applying such model, we explore the role of domain dynamics in HfO2 and find that the fatigue and imprint phenomena are closely related to the so-called E-path and T-path switching pathways. Based on the different atomic motions in the two paths, we propose that an inclined electric field can sufficiently suppress fatigue and enhancing the performance of HfO2-based ferroelectric devices.

The bilayer nickelate La$_3$Ni$_2$O$_7$ (LNO) exhibits a remarkably high-temperature superconductivity of approximately 80 K under pressure, sparking considerable attentions. However, the nature of the spin, charge, and orbital order in LNO remains unknown, hindering the exploration of the mechanism of superconductivity. Here, supported by symmetry analysis and density functional theory calculations, we unravel a double strip ground state of LNO with alternating magnetic moments arrangements. Interestingly, a charge density wave emerges under this determined magnetic order. Such CDW phase exhibits a Pmnm space group with breathing deformation of the NiO$_6$ octahedra, where the stretching and shrinking of the octahedra correspond to the formation of \emph{d}$_{z^2}^1$\emph{d}$_{x^2-y^2}^1$ and \emph{d}$_{z^2}^1$ orbital order. Moreover, we build a first-principles-based Hamiltonian for LNO, which provides deeper insight into its peculiar magnetic order. Our work thus reveals a systematic spin-charge-orbital picture for LNO, which can be extended to other nickelate-based superconductors, paving the way for determining the mechanism of superconductivity., Comment: 6 oages, 4 figures

Type-II multiferroicity, where electric polarization is induced by specific spin patterns, is crucial in fundamental physics and advanced spintronics. However, the spin model and magnetoelectric coupling mechanisms in prototypical type-II multiferroic CuFeO$_2$ and Al-doped CuFeO$_2$ remain unclear. Here, by considering both spin and alloy degrees of freedom, we develop a magnetic cluster expansion method, which considers all symmetry allowed interactions. Applying such method, we not only obtain realistic spin model that can correctly reproduce observations for both CuFeO$_2$ and CuFe$_{1-x}$Al$_x$O$_2$, but also revisit well-known theories of the original spin-current (SC) model and $p$-$d$ hybridization model. Specifically, we find that (i) a previously overlooked biquadratic interaction is critical to reproduce the $\uparrow\uparrow\downarrow\downarrow$ ground state and excited states of CuFeO$_2$; (ii) the combination of absent biquadratic interaction and increased magnetic frustration around Al dopants stabilizes the proper screw state; and (iii) it is the generalized spin-current (GSC) model that can correctly characterize the multiferroicity of CuFeO$_2$. These findings have broader implications for understanding novel magnetoelectric couplings in, e.g., monolayer multiferroic NiI$_2$.

Recent studies highlight the scientific importance and broad application prospects of two-dimensional (2D) sliding ferroelectrics, which prevalently exhibit vertical polarization with suitable stackings. It is crucial to understand the mechanisms of sliding ferroelectricity and to deterministically and efficiently switch the polarization with optimized electric fields. Here, applying our newly developed DREAM-Allegro multi-task equivariant neural network, which simultaneously predicts interatomic potentials and Born effective charges, we construct a comprehensive potential for boron nitride ($\mathrm{BN}$) bilayer. The molecular dynamics simulations reveal a remarkably high Curie temperature of up to 1500K, facilitated by robust intralayer chemical bonds and delicate interlayer van der Waals(vdW) interactions. More importantly, it is found that, compared to the out-of-plane electric field, the inclined field not only leads to deterministic switching of electric polarization, but also largely lower the critical strength of field, due to the presence of the in-plane polarization in the transition state. This strategy of an inclined field is demonstrated to be universal for other sliding ferroelectric systems with monolayer structures belonging to the symmetry group $p \bar{6} m 2$, such as transition metal dichalcogenides (TMDs)., Comment: Main text: 16 pages, 4 figures. Supplementary: 9 pages, 6 figures

Ferroelectric nitrides have emerged as promising semiconductor materials for modern electronics. However, their domain structures and associated properties are basically unknown, despite their potential to result in optimized or new phenomena. Density functional theory calculations are performed to investigate the effect of epitaxial strain on multidomains of (Al,Sc)N nitride systems and to compare it with the monodomain case. The multidomain systems are predicted to have five strain-induced regions, to be denoted as Regions I to V, respectively. Each of these regions is associated with rather different values or behaviors of physical properties such as axial ratio, polarizations, internal parameters, bond lengths, etc. Of particular interest is the prediction of bent domains under compressive strain extending beyond $-$5.5%, which indicates that domain walls may play a key role in the mechanical failure properties of these systems. Interestingly, such bending induces the creation of a finite in-plane polarization (in addition to out-of-plane dipoles) due to geometric and symmetry considerations. Strikingly too, the bent domains have lower energy than the wurtzite monodomains and have atomically sharp boundaries. Our findings may pave the way for domain wall engineering in ferroelectric nitrides., Comment: 9 pages, 4 figures

Utilizations of silicon-based luminescent devices are restricted by the indirect-gap nature of diamond silicon. In this study, the high-throughput method is employed to expedite discoveries of direct-gap silicon crystals. The machine learning (ML) potential is utilized to construct a dataset comprising 2637 silicon allotropes, which is subsequently screened using an ML Hamiltonian model and density functional theory calculations, resulting in identification of 47 direct-gap Si structures. We calculate transition dipole moments (TDM), energies, and phonon bandstructures of these structures to validate their performance. Additionally, we recalculate bandgaps of these structures employing the HSE06 functional. 22 silicon allotropes are identified as potential photovoltaic materials. Among them, the energy per atom of Si22-Pm, which has a direct bandgap of 1.27 eV, is 0.026 eV/atom higher than diamond silicon. Si18-C2/m, which has a direct bandgap of 0.796 eV, exhibits the highest TDM among identified structures. Si16-P21/c, which has a direct bandgap of 0.907 eV, has the mass density of 2.316 g/cm3, which is the highest among identified structures and higher than that of diamond silicon. The structure Si12-P1, which possesses a direct bandgap of 1.69 eV, exhibits the highest spectroscopic limited maximum efficiency (SLME) among identified structures at 32.28%, surpassing that of diamond silicon. This study offers insights into properties of silicon crystals while presenting a systematic high-throughput method for material discovery.

Recent advancements underscore the critical need to develop ferroelectric materials compatible with silicon. We systematically explore possible ferroelectric silicon quantum films and discover a low-energy variant (hex-OR-2*2-P) with energy just 1 meV/atom above the ground state (hex-OR-2*2). Both hex-OR-2*2 and hex-OR-2*2-P are confirmed to be dynamically and mechanically stable semiconductors with indirect gaps of 1.323 eV and 1.311 eV, respectively. The ferroelectric hex-OR-2*2-P exhibits remarkable in-plane spontaneous polarization up to 120 Pc/m and is protected by a potential barrier (13.33 meV/atom) from spontaneously transitioning to hex-OR-22. To simulate the switching ferroelectricity in electric fields of the single-element silicon bilayer, we develop a method that simultaneously learns interatomic potentials and Born effective charges (BEC) in a single equivariant model with a physically informed loss. Our method demonstrates good performance on several ferroelectrics. Simulations of hex-OR-2*2-P silicon suggest a depolarization temperature of approximately 300 K and a coercive field of about 0.05 V/{\AA}. These results indicate that silicon-based ferroelectric devices are feasible, and the ground state phase of the silicon bilayer (hex-OR-2*2) is an ideal system. Our findings highlight the promise of pure silicon ferroelectric materials for future experimental synthesis and applications in memory devices, sensors, and energy converters., Comment: 18 pages, 3 figures

We systematically investigate the effects of Kitaev interaction on magnetic orders and anisotropy in both triangular and honeycomb lattices. Our study highlights the critical role of the Kitaev interaction in modulating phase boundaries and predicting new phases, e.g., zigzag phase in triangular lattice and AABB phase in honeycomb lattice, which are absent with pure Heisenberg interactions. Moreover, we reveal the special state-dependent anisotropy of Kitaev interaction, and develop a general method that can determine the presence of Kitaev interaction in different magnets. It is found that the Kitaev interaction does not induce anisotropy in some magnetic orders such as ferromagnetic order, while can cause different anisotropy in other magnetic orders. Furthermore, we emphasize that the off-diagonal $\Gamma$ interaction also contributes to anisotropy, competing with the Kitaev interaction to reorient spin arrangements. Our work establishes a framework for comprehensive understanding the impact of Kitaev interaction on ordered magnetism.

Kitaev spin liquid is proposed to be promisingly realized in low spin-orbit coupling $3d$ systems, represented by Na$_3$Co$_2$SbO$_6$ and Na$_3$Ni$_2$BiO$_6$. However, the existence of Kitaev interaction is still debatable among experiments, and obtaining the strength of Kitaev interaction from first-principles calculations is also challenging. Here, we report the state-dependent anisotropy of Kitaev interaction, based on which a convenient method is developed to rapidly determine the strength of Kitaev interaction. Applying such method and density functional theory calculations, it is found that Na$_3$Co$_2$SbO$_6$ with $3d^7$ configuration exhibits considerable ferromagnetic Kitaev interaction. Moreover, by further applying the symmetry-adapted cluster expansion method, a realistic spin model is determined for Na$_3$Ni$_2$BiO$_6$ with $3d^8$ configuration. Such model indicates negligible small Kitaev interaction, but it predicts many properties, such as ground states and field effects, which are well consistent with measurements. Furthermore, we demonstrate that the heavy elements, Sb or Bi, located at the hollow sites of honeycomb lattice, do not contribute to emergence of Kitaev interaction through proximity, contradictory to common belief. The presently developed anisotropy method will be beneficial not only for computations but also for measurements.

Electro-optic (EO) effects relate the change of optical constants by low-frequency electric fields. Thanks to the advent of Density Functional Perturbation Theory (DFPT), the EO properties of bulk three-dimensional (3D) materials can now be calculated in an ab initio way. However, the use of periodic boundary conditions in most Density Functional Theory codes imposes to simulate two-dimensional (2D) materials using slabs surrounded by a large layer of vacuum. The EO coefficients predicted from such calculations, if not rescaled properly, can severely deviate from the real EO properties of 2D materials. The present work discusses the issue and introduces the rescaling relationships allowing to recover the true EO properties., Comment: 18 pages, 4 figures

While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the Kohn-Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations. Despite advancements, challenges such as the necessity for computing extensive DFT training data to explore each new system and the complexity of establishing accurate ML models for multi-elemental materials still exist. Addressing these hurdles, this study introduces a universal electronic Hamiltonian model trained on Hamiltonian matrices obtained from first-principles DFT calculations of nearly all crystal structures on the Materials Project. We demonstrate its generality in predicting electronic structures across the whole periodic table, including complex multi-elemental systems, solid-state electrolytes, Moir\'e twisted bilayer heterostructure, and metal-organic frameworks (MOFs). Moreover, we utilize the universal model to conduct high-throughput calculations of electronic structures for crystals in GeNOME datasets, identifying 3,940 crystals with direct band gaps and 5,109 crystals with flat bands. By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields, such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible., Comment: 20 pages, 9 figures

Recently, two monolayer magnetic materials, i.e., FePS3 and NiPS3, have been successfully fabricated. Despite that they have the same atomic structure, the two monolayers exhibit distinct magnetic properties. FePS3 holds an out-of-plane zigzag antiferromagnetic (AFM-ZZ) structure, while NiPS3 exhibits an in-plane AFM-ZZ structure. However, there is no theoretical model which can properly describe its magnetic ground state due to the lack of a full understanding of its magnetic interactions. Here, by combining the first-principles calculations and the newly developed machine learning method, we construct an exact spin Hamiltonian of the two magnetic materials. Different from the previous studies which failed to fully consider the spin-orbit coupling effect, we find that the AFM-ZZ ground state in FePS3 is stabilized by competing ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor exchange interactions, and combining single-ion anisotropy. Whereas, the often ignored nearest-neighbor biquadratic exchange is responsible for the in-plane AFM-ZZ ground state in NiPS3. We additionally calculate spin-wave spectrum of AFM-ZZ structure in the two monolayers based on the exact spin Hamiltonian, which can be directly verified by the experimental investigation. Our work provides a theoretical framework for the origin of AFM-ZZ ground state in two-dimensional materials., Comment: 7 pages, 4 figures

The demand for high-density storage is urgent in the current era of data explosion. Recently, several single-molecule (-atom) magnets/ferroelectrics have been reported to be promising candidates for high-density storage. As another promising candidate, single-molecule multiferroics are not only small but also possess ferroelectric and magnetic orderings, which can sometimes be strongly coupled and used as data storages to realize the combination of electric writing and magnetic reading. However, they have been rarely proposed, and never been experimentally reported. Here, by building Hamiltonian models, we propose a new model of single-molecule multiferroic in which electric dipoles and magnetic moments are parallel and can rotate with the rotation of the single molecule. Furthermore, with performing spin-lattice dynamics simulations, we reveal the conditions (e.g., large enough single-ion anisotropy and appropriate electric field) under which the new single-molecule multiferroic can arise. Based on this model, as well as first-principles calculations, a realistic example Co(NH3)4N@SWCNT is constructed and numerically confirmed to demonstrate the feasibility of the new single-molecule multiferroic model. Our work not only sheds light on the discovery of single-molecule multiferroics but also provides a new guideline to design multifunctional materials for ultimate memory devices.

Magnetic damping has a significant impact on the performance of various magnetic and spintronic devices, making it a long-standing focus of research. The strength of magnetic damping is usually quantified by the Gilbert damping constant in the Landau-Lifshitz-Gilbert equation. Here we propose a first-principles based approach to evaluate the Gilbert damping constant contributed by spin-lattice coupling in magnetic insulators. The approach involves effective Hamiltonian models and spin-lattice dynamics simulations. As a case study, we applied our method to Y$_3$Fe$_5$O$_{12}$, MnFe$_2$O$_4$ and Cr$_2$O$_3$. Their damping constants were calculated to be $0.8\times10^{-4}$, $0.2\times10^{-4}$, $2.2\times 10^{-4}$, respectively at a low temperature. The results for Y$_3$Fe$_5$O$_{12}$ and Cr$_2$O$_3$ are in good agreement with experimental measurements, while the discrepancy in MnFe$_2$O$_4$ can be attributed to the inhomogeneity and small band gap in real samples. The stronger damping observed in Cr$_2$O$_3$, compared to Y$_3$Fe$_5$O$_{12}$, essentially results from its stronger spin-lattice coupling. In addition, we confirmed a proportional relationship between damping constants and the temperature difference of subsystems, which had been reported in previous studies. These successful applications suggest that our approach serves as a promising candidate for estimating the Gilbert damping constant in magnetic insulators., Comment: 14 pages, 11 figures

Using machine learning methods, we explore different types of domain walls in the recently unveiled single-element ferroelectric, the bismuth monolayer [Nature 617, 67 (2023)]. Remarkably, our investigation reveals that the charged domain wall configuration exhibits lower energy compared to the uncharged domain wall structure. We also demonstrate that the experimentally discovered tail-to-tail domain wall maintains topological interfacial states caused by the change in the Z_2 number between ferroelectric and paraelectric states. Interestingly, due to the intrinsic built-in electric fields in asymmetry DW configurations, we find that the energy of topological interfacial states splits, resulting in an accidental band crossing at the Fermi level. Our study suggests that domain walls in two-dimensional bismuth hold potential as a promising platform for the development of ferroelectric domain wall devices., Comment: 15 pages, 4 figs

Kagome materials provide a promising platform to explore intriguing correlated phenomena including magnetism, charge density wave (CDW), and nontrivial band topology. Recently, a CDW order was observed in antiferromagnetic kagome metal FeGe, sparking enormous research interests in intertwining physics of CDW and magnetism. Two of the core questions are (i) what are the driving forces of the CDW transition in FeGe and (ii) whether magnetism play a critical role in the transition. Such questions are critical as conventional mechanisms of van Hove singularities and Fermi surface nesting fail to explain the stable pristine phase, as well as the role of magnetism. Here, supported by density functional theory and tight-binding models, we unravel the triple-well CDW energy landscape of FeGe, indicating that both the pristine and CDW phases are locally stable. We point out that an entire downward shift of Ge band, instead of the previously proposed Fe bands, competes with the lattice distortion energy, driving the triple-well CDW transition. It is indeed a cooperation between the Peierls-like effect and the Fermi energy pinning phenomenon, which is distinct from the conventional Peierls effect that drives a double-well transition. Moreover, we demonstrate that the antiferromagnetic order also plays a critical role in driving the CDW transition, through weakening the Fe-Ge hybridization by exchange splitting and lowering the position of Ge-bands with respect to the Fermi energy. Our work thus not only deepens the understanding of the CDW mechanism in FeGe, but also indicates an intertwined connection between the emergent magnetism and CDW in kagome materials., Comment: 25 pages,4 figure

The study of the electronic properties of charged defects is crucial for our understanding of various electrical properties of materials. However, the high computational cost of density functional theory (DFT) hinders the research on large defect models. In this study, we present an E(3) equivariant graph neural network framework (HamGNN-Q), which can predict the tight-binding Hamiltonian matrices for various defect types with different charges using only one set of network parameters. By incorporating features of background charge into the element representation, HamGNN-Q enables a direct mapping from structure and background charge to the electronic Hamiltonian matrix of charged defect systems without DFT calculation. We demonstrate the model's high precision and transferability through testing on GaAs systems with various charged defect configurations. Our approach provides a practical solution for accelerating charged defect electronic structure calculations and advancing the design of materials with tailored electronic properties., Comment: 16 pages, 7 figures

Complex spin-spin interactions in magnets can often lead to magnetic superlattices with complex local magnetic arrangements, and many of the magnetic superlattices have been found to possess non-trivial topological electronic properties. Due to the huge size and complex magnetic moment arrangement of the magnetic superlattices, it is a great challenge to perform a direct DFT calculation on them. In this work, an equivariant deep learning framework is designed to accelerate the electronic calculation of magnetic systems by exploiting both the equivariant constraints of the magnetic Hamiltonian matrix and the physical rules of spin-spin interactions. This framework can bypass the costly self-consistent iterations and build a direct mapping from a magnetic configuration to the ab initio Hamiltonian matrix. After training on the magnets with random magnetic configurations, our model achieved high accuracy on the test structures outside the training set, such as spin spiral and non-collinear antiferromagnetic configurations. The trained model is also used to predict the energy bands of a skyrmion configuration of NiBrI containing thousands of atoms, showing the high efficiency of our model on large magnetic superlattices., Comment: 15 pages, 5 figures

Electron-phonon coupling (EPC) plays an important role in many fundamental physical phenomena, but the high computational cost of the EPC matrix hinders the theoretical research on them. In this paper, an analytical formula is derived to calculate the EPC matrix in terms of the Hamiltonian and its gradient in the nonorthogonal atomic orbital bases. The recently-developed E(3) equivariant neural network is used to directly predict the Hamiltonian and its gradient needed by the formula, thus bypassing the expensive self-consistent iterations in DFT. The correctness of the proposed EPC calculation formula and the accuracy of the predicted EPC values of the network are illustrated by the tests on a water molecule and a MoS2 crystal., Comment: 11 pages, 2 figures, 2 tables

Kagome metals AV3Sb5 (A=K,Rb,or Cs) exhibit intriguing charge density wave (CDW) instabilities, which interplay with superconductivity and band topology. However, despite firm observations, the atomistic origins of the CDW phases, as well as hidden instabilities, remain elusive. Here, we adopt our newly developed symmetry-adapted cluster expansion method to construct a first-principles-based effective Hamiltonian of CsV3Sb5, which not only reproduces the established inverse star of David (ISD) phase, but also predict a series of D3h-n states under mild tensile strains. With such atomistic Hamiltonians, the microscopic origins of different CDW states are revealed as the competition of the second-nearest neighbor V-V pairs versus the first-nearest neighbor V-V and V-Sb couplings. Interestingly, the effective Hamiltonians also reveal the existence of ionic Dzyaloshinskii-Moriya interaction in the high-symmetry phase of CsV3Sb5 and drives the formation of non-collinear CDW patterns. Our work thus not only deepens the understanding of the CDW formation in AV3Sb5,but also demonstrates that the effective Hamiltonian is a suitable approach for investigating CDW mechanisms, which can be extended to various CDW systems., Comment: 6 pages, 4 figures, 1 tables

Machine Learning (ML) interatomic models and potentials have been widely employed in simulations of materials. Long-range interactions often dominate in some ionic systems whose dynamics behavior is significantly influenced. However, the long-range effect such as Coulomb and Van der Wales potential is not considered in most ML interatomic potentials. To address this issue, we put forward a method that can take long-range effects into account for most ML local interatomic models with the reciprocal space neural network. The structure information in real space is firstly transformed into reciprocal space and then encoded into a reciprocal space potential or a global descriptor with full atomic interactions. The reciprocal space potential and descriptor keep full invariance of Euclidean symmetry and choice of the cell. Benefiting from the reciprocal-space information, ML interatomic models can be extended to describe the long-range potential including not only Coulomb but any other long-range interaction. A model NaCl system considering Coulomb interaction and the GaxNy system with defects are applied to illustrate the advantage of our approach. At the same time, our approach helps to improve the prediction accuracy of some global properties such as the band gap where the full atomic interaction beyond local atomic environments plays a very important role. In summary, our work has expanded the ability of current ML interatomic models and potentials when dealing with the long-range effect, hence paving a new way for accurate prediction of global properties and large-scale dynamic simulations of systems with defects., Comment: 15 pages, 3 figures, 3 tables

Besides moir\'e superlattice, twisting can also generate moir\'e magnetic exchange interactions (MMEIs) in van der Waals magnets. However, due to the extreme complexity and twist-angle-dependent sensitivity, all existing models fail to capture the MMEIs, preventing the understanding of MMEIs-induced new physics. Here, we develop a microscopic moir\'e spin Hamiltonian that enables the effective description of MMEIs via a sliding-mapping approach in twisted magnets, as demonstrated in twisted bilayer CrI3. Unexpectedly, we discover that the emergence of MMEIs can create an unprecedented magnetic skyrmion bubble (SkB) with non-conversed helicity, named as moir\'e-type SkB, representing a unique spin texture solely generated by MMEIs and ready to be detected under the current experimental conditions. Importantly, the size and population of SkBs can be finely controlled by twist angle, a key step for skyrmion-based quantum computing and information storage. Furthermore, we reveal that the MMEIs can be effectively manipulated by the substrate-induced interfacial Dzyaloshinskii-Moriya interaction, modulating the twist-angle-dependent magnetic phase diagram, which solves the outstanding disagreements between prior theories and experiments and verifies our theory., Comment: 17 pages, 5 figures

A realistic first-principle-based spin Hamiltonian is constructed for the type-II multiferroic NiI$_2$, using a symmetry-adapted cluster expansion method. Besides single ion anisotropy and isotropic Heisenberg terms, this model further includes the Kitaev interaction and a biquadratic term, and can well reproduce striking features of the experimental helical ground state, that are, {\it e.g.}, a proper screw state, canting of rotation plane, propagation direction and period. Using this model to build a phase diagram, it is demonstrated that, (i) the in-plane propagation direction of $\langle1\bar10\rangle$ is determined by the Kitaev interaction, instead of the long-believed exchange frustrations; and (ii) the canting of rotation plane is also dominantly determined by Kitaev interaction, rather than interlayer couplings. Furthermore, additional Monte Carlo simulations reveal three equivalent domains and different topological defects. Since the ferroelectricity is induced by spins in type-II multiferroics, our work also implies that Kitaev interaction is closely related to the multiferroicity of NiI$_2$.

This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments. SpinGNN++ integrates multitask spin equivariant neural network with explicit spin-lattice terms, including Heisenberg, Dzyaloshinskii-Moriya, Kitaev, single-ion anisotropy, and biquadratic interactions, and employs time-reversal equivariant neural network to learn high-order spin-lattice interactions using time-reversal E(3)-equivariant convolutions. To validate SpinGNN++, a complex magnetic model dataset is introduced as a benchmark and employed to demonstrate its capabilities. SpinGNN++ provides accurate descriptions of the complex spin-lattice coupling in monolayer CrI$_3$ and CrTe$_2$, achieving sub-meV errors. Importantly, it facilitates large-scale parallel spin-lattice dynamics, thereby enabling the exploration of associated properties, including the magnetic ground state and phase transition. Remarkably, SpinGNN++ identifies a new ferrimagnetic state as the ground magnetic state for monolayer CrTe2, thereby enriching its phase diagram and providing deeper insights into the distinct magnetic signals observed in various experiments., Comment: 27 pages,6 figures and 3 tables

Strongly correlated materials often exhibit an electronic phase separation (EPS) phenomena whose domain pattern is random in nature. The ability to control the spatial arrangement of the electronic phases at microscopic scales is highly desirable for tailoring their macroscopic properties and/or designing novel electronic devices. Here we report the formation of EPS nanoscale network in a mono-atomically stacked LaMnO3/CaMnO3/PrMnO3 superlattice grown on SrTiO3 (STO) (001) substrate, which is known to have an antiferromagnetic (AFM) insulating ground state. The EPS nano-network is a consequence of an internal strain relaxation triggered by the structural domain formation of the underlying STO substrate at low temperatures. The same nanoscale network pattern can be reproduced upon temperature cycling allowing us to employ different local imaging techniques to directly compare the magnetic and transport state of a single EPS domain. Our results confirm the one-to-one correspondence between ferromagnetic (AFM) to metallic (insulating) state in manganite. It also represents a significant step in a paradigm shift from passively characterizing EPS in strongly correlated systems to actively engaging in its manipulation.

Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations. In this work, we proposed a general analytic Hamiltonian representation in an E(3) equivariant framework, which can fit the ab initio Hamiltonian of molecules and solids by a complete data-driven method and are equivariant under rotation, space inversion, and time reversal operations. Our model reached state-of-the-art precision in the benchmark test and accurately predicted the electronic Hamiltonian matrices and related properties of various periodic and aperiodic systems, showing high transferability and generalization ability. This framework provides a general transferable model that can be used to accelerate the electronic structure calculations on different large systems with the same network weights trained on small structures., Comment: 33 pages, 6 figures

Magnetics, ferroelectrics and multiferroics have attracted great attentions because they are not only extremely important for investigating fundamental physics, but also have important applications in information technology. Here, recent computational studies on magnetism and ferroelectricity are reviewed. We first give a brief introduction to magnets, ferroelectrics, and multiferroics. Then, theoretical models and corresponding computational methods for investigating these materials are presented. In particular, a new method for computing the linear magnetoelectric coupling tensor without applying an external field in the first principle calculations is proposed for the first time. The functionalities of our homemade Property Analysis and Simulation Package for materials (PASP) and its applications in the field of magnetism and ferroelectricity are discussed. Finally, we summarize this review and give a perspective on possible directions of future computational studies on magnetism and ferroelectricity.

A new set of lattice-models based on the hexagonal $\sqrt{N}\times\sqrt{N}$ super-cells of the well-known honeycomb lattice with single-hole defect (HL-D-1/2N) are proposed to realize the nontrivial isolated flat-bands. Through performing both tight-binding and density functional theory calculations, we demonstrate that the experimentally realized graphitic carbon nitride (Adv. Mater., 22, 1004, 2010; Nat. Commun., 9, 3366, 2018), the HL-D-1/8 based C$_4$N$_3$, is a perfect system to host such flat bands. For the flat high-energy P-6m2 C$_4$N$_3$ structure, it displays the ferromagnetic half-metallicity which is not related to the isolated flat bands. However, the P-6m2 C$_4$N$_3$ structure is dynamically unstable. Using a structure searching method based on group and graph theory, we find that a new corrugated Pca21 C4N3 structure has the lowest energy among all known C$_4$N$_3$ structures. This Pca21 C$_4$N$_3$ structure is an intrinsic ferromagnetic half-semiconductor (Tc$\approx$241 K) with one semiconducting spin-channel (1.75 eV) and one insulating spin-channel (3.64 eV), which is quite rare in the two-dimensional (2D) systems. Its ferromagnetic semiconducting property originates from the isolated p$_z$-state flat-band as the corrugation shift the flat band upward to the Fermi level. Interestingly, this Pca21 C$_4$N$_3$ structure is found to be piezoelectric and ferroelectric, which makes C$_4$N$_3$ an unusual transition-metal-free 2D multiferroic., Comment: 6 pages, 4 fingures

Organic-inorganic multiferroics are promising for the next generation of electronic devices. To date, dozens of organic-inorganic multiferroics have been reported; however, most of them show magnetic Curie temperature much lower than room temperature, which drastically hampers their application. Here, by performing first-principle calculations and building effective model Hamiltonians, we reveal a molecular orbital-mediated magnetic coupling mechanism in two-dimensional Cr(pyz)2 (pyz=pyrazine), and the role that the valence state of the molecule plays in determining the magnetic coupling type between metal ions. Based on these, we demonstrate that a two-dimensional organic-inorganic room-temperature multiferroic, Cr(h-fpyz)2 (h-fpyz= half-fluoropyrazine), can be rationally designed by introducing ferroelectricity in Cr(pyz)2 while keeping the valence state of the molecule unchanged. Our work not only reveals the origin of magnetic coupling in 2D organic-inorganic systems, but also provides a way to design room temperature multiferroic materials rationally.

Based on a simple tight-binding model, we propose a general theory of light-induced magnetic phase transition (MPT) in antiferromagnets based on the general conclusion that the bandgap of antiferromagnetic (AFM) phase is usually larger than that of ferromagnetic (FM) one in a given system. Light-induced electronic excitation prefers to stabilize the FM state over the AFM one, and once the critical photocarrier concentration ({\alpha}_c) is reached, an MPT from AFM phase to FM phase takes place. This theory has been confirmed by performing first-principles calculations on a series of two-dimensional (2D) van der Waals (vdW) antiferromagnets and a linear relationship between {\alpha}_c and the intrinsic material parameters is obtained. Importantly, our conclusion is still valid even considering the strong exciton effects during photoexcitation. Our general theory provides new ideas to realize reversible read-write operations for future memory devices., Comment: 14 pages, 3 figures

While nature provides a plethora of perovskite materials, only a few exhibits large ferroelectricity and possibly multiferroicity. The majority of perovskite materials have the non-polar CaTiO$_3$(CTO)structure, limiting the scope of their applications. Based on effective Hamiltonian model as well as first-principles calculations, we propose a general thin-film design method to stabilize the functional BiFeO$_3$(BFO)-type structure, which is a common metastable structure in widespread CaTiO$_3$-type perovskite oxides. It is found that the improper antiferroelectricity in CTO-type perovskite and ferroelectricity in BFO-type perovskite have distinct dependences on mechanical and electric boundary conditions, both of which involve oxygen octahedral rotation and tilt. The above difference can be used to stabilize the highly polar BFO-type structure in many CTO-type perovskite materials.

The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potentials for magnetic systems that account for both magnetic moments and structural degrees of freedom remains a challenge. This work introduces SpinGNN, a spin-dependent interatomic potential approach that employs the graph neural network (GNN) to describe magnetic systems. SpinGNN consists of two types of edge GNNs: Heisenberg edge GNN (HEGNN) and spin-distance edge GNN (SEGNN). HEGNN is tailored to capture Heisenberg-type spin-lattice interactions, while SEGNN accurately models multi-body and high-order spin-lattice coupling. The effectiveness of SpinGNN is demonstrated by its exceptional precision in fitting a high-order spin Hamiltonian and two complex spin-lattice Hamiltonians with great precision. Furthermore, it successfully models the subtle spin-lattice coupling in BiFeO3 and performs large-scale spin-lattice dynamics simulations, predicting its antiferromagnetic ground state, magnetic phase transition, and domain wall energy landscape with high accuracy. Our study broadens the scope of graph neural network potentials to magnetic systems, serving as a foundation for carrying out large-scale spin-lattice dynamic simulations of such systems., Comment: 28 pages, 4 figures

Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become the next-generation material simulation tool after the density functional theory (DFT). However, there is currently a lack of a general MPNN framework for directly predicting the tensor properties of the crystals. In this work, a general framework for the prediction of tensor properties was proposed: the tensor property of a crystal can be decomposed into the average of the tensor contributions of all the atoms in the crystal, and the tensor contribution of each atom can be expanded as the sum of the tensor projections in the directions of the edges connecting the atoms. On this basis, the edge-based expansions of force vectors, Born effective charges (BECs), dielectric (DL) and piezoelectric (PZ) tensors were proposed. These expansions are rotationally equivariant, while the coefficients in these tensor expansions are rotationally invariant scalars which are similar to physical quantities such as formation energy and band gap. The advantage of this tensor prediction framework is that it does not require the network itself to be equivariant. Therefore, in this work, we directly designed the edge-based tensor prediction graph neural network (ETGNN) model on the basis of the invariant graph neural network to predict tensors. The validity and high precision of this tensor prediction framework were shown by the tests of ETGNN on the extended systems, random perturbed structures and JARVIS-DFT datasets. This tensor prediction framework is general for nearly all the GNNs and can achieve higher accuracy with more advanced GNNs in the future., Comment: 19 pages, 3 figures

Hexagonal LuFe2O4 is a promising charge-order (CO) driven multiferroic material with high charge and spin ordering temperatures. The coexisting charge and spin orders on Fe3+/Fe2+ sites result in novel magnetoelectric behaviors, but the coupling mechanism between the charge and spin orders remains elusive. Here, by tuning external pressure, we reveal three correlated spin-charge ordered phases in LuFe2O4: i) a centrosymmetric incommensurate three-dimensional CO with ferrimagnetism, ii) a non-centrosymmetric incommensurate quasi-two-dimensional CO with ferrimagnetism, and iii) a centrosymmetric commensurate CO with antiferromagnetism. Experimental in-situ single-crystal X-ray diffraction and X-ray magnetic circular dichroism measurements combined with density functional theory calculations suggest that the charge density redistribution caused by pressure-induced compression in the frustrated double-layer [Fe2O4] cluster is responsible for the correlated spin-charge phase transitions. The pressure-enhanced effective Coulomb interactions among Fe-Fe bonds drive the frustrated (1/3, 1/3) CO to a less frustrated (1/4, 1/4) CO, which induces the ferrimagnetic to antiferromagnetic transition. Our results not only elucidate the coupling mechanism among charge, spin and lattice degrees of freedom in LuFe2O4 but also provide a new way to tune the spin-charge orders in a highly controlled manner.