arXiv subjects

Hongjun Xiang

Publications and source records attributed to Hongjun Xiang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Intrinsic breakdown strength: theoretical derivation and first-principles calculations

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.

cond-mat.mtrl-sci↗

arXiv2024

Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks

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.

physics.comp-ph↗

arXiv2024

Advancing Nonadiabatic Molecular Dynamics Simulations for Solids: Achieving Supreme Accuracy and Efficiency with Machine Learning

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.

physics.comp-ph↗

arXiv2024

Role of Domain Walls on Imprint and Fatigue in HfO2-Based Ferroelectrics

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.

cond-mat.mtrl-sci↗

arXiv2024

Emergent spin-charge-orbital order in superconductor La$_3$Ni$_2$O$_7$

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.

cond-mat.supr-con↗

arXiv2024

Mechanism of Type-II Multiferroicity in Pure and Al-Doped CuFeO$_2$

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$_2$.

cond-mat.mtrl-sci↗

arXiv2024

Deterministic and Efficient Switching of Sliding Ferroelectrics

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).

cond-mat.mtrl-sci↗

arXiv2024

Strain-induced bent domains in ferroelectric nitrides

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.

cond-mat.mtrl-sci↗

arXiv2024

Identifying Direct Bandgap Silicon Structures with High-throughput Search and Machine Learning Methods

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.

cond-mat.mtrl-sci↗

arXiv2024

Switchable Ferroelectricity in Subnano Silicon Thin Films

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/Å. 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.

cond-mat.mtrl-sci↗

arXiv2024

Strength of Kitaev Interaction in Na$_3$Co$_2$SbO$_6$ and Na$_3$Ni$_2$BiO$_6$

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.

cond-mat.str-el↗

arXiv2024

Effects of Kitaev Interaction on Magnetic Orders and Anisotropy

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 $Γ$ 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.

cond-mat.str-el↗

arXiv2024

Topological interfacial states in ferroelectric domain walls of two-dimensional bismuth

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.

cond-mat.mtrl-sci↗

arXiv2024

Universal Machine Learning Kohn-Sham Hamiltonian for Materials

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, Moiré 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.

physics.comp-ph↗

arXiv2024

Electro-optic properties from ab initio calculations in two-dimensional materials

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.

cond-mat.mtrl-sci↗

arXiv2024

Atomistic Origin of Diverse Charge Density Wave States in CsV$_3$Sb$_5$

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.

cond-mat.mtrl-sci↗

arXiv2024

General time-reversal equivariant neural network potential for magnetic materials

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.

cond-mat.mtrl-sci↗

arXiv2024

Origin of zigzag antiferromagnetic orders in XPS3 (X= Fe, Ni) monolayers

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.

cond-mat.mtrl-sci↗