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Hongjun Xiang

Publications and source records attributed to Hongjun Xiang.

At least 19 recordsLinked to original sources
arXiv2026

First-Principles Electronic Structure Calculation of Crystals in Laboratory Magnetic Fields

External magnetic fields can qualitatively reshape the electronic structure of crystals, underpinning quantum Hall physics, Landau-level spectra and field-induced topological phases. Their first-principles treatment at laboratory-scale fields is, however, hindered by magnetic-flux quantization, which requires magnetic unit cells with areas inversely proportional to the applied field. Such cells contain a large number of chemical unit cells, rendering real-space and plane-wave calculations prohibitively expensive. Here we, for the first time, construct a magnetic Bloch basis built from linear combinations of gauge-including Gaussian-type atomic orbitals, which incorporate the magnetic-field phase factors required by magnetic translation symmetry. The framework requires far fewer basis functions than real-space or plane-wave representations of the same magnetic supercell and retains the sparsity of an atom-centred basis, together substantially reducing computational cost. We validate the framework by reproducing Landau-level spectrum of graphene from first principles. This approach provides a practical route to simulations of crystalline materials under experimentally accessible magnetic fields.

cond-mat.mtrl-sci↗
arXiv2026

A Physical Response-and-Memory Model for Muon Optimization

Training large language models is costly. How low a loss the same compute can ultimately reach depends on how each step's gradient is converted into a weight update; the rule that performs this conversion is the optimizer. From SGD and AdamW to the recent Muon, effective update rules have mostly been shaped by engineering intuition and then selected on benchmarks. Muon semi-orthogonalizes the momentum matrix before applying the update and has kept breaking records on public training benchmarks; yet why the semi-orthogonalized direction works, and over how long a history the momentum should average, are two questions at present answered mainly by experience. Here we treat the weight matrix during training as a responsive medium with memory and build a physical model for it, in which both questions find answers: the semi-orthogonalized direction is the maximally dissipative response under an output-side safety budget, which explains why it works; momentum is the internal stress accumulated by the medium; how long it should average is set by the relaxation of this stress, and a real medium relaxes on more than one timescale, the simplest form being one fast and one slow. On this basis we propose the Bi-Maxwell optimizer. The framework further yields a testable consequence: gradient directions change fast early in training and more slowly later, so the optimal memory length should grow with training stage; step-by-step measurements of a proxy for it by a read-only probe across 8 independent training trajectories are consistent with this consequence. Replacing the memory kernel alone, from a single timescale to two, brings training to the target loss in noticeably fewer steps on a public large-language-model optimizer benchmark.

cs.LG↗
arXiv2026

First-Principles Electron-Magnon Coupling with Machine-Learning Hamiltonians: From Band Renormalization to Transport

In analogy to electron-phonon coupling (EPC), electron-magnon coupling (EMC) is expected to shape electronic structure, transport, and possibly unconventional superconductivity in magnetic materials. However, unlike EPC, which is now routinely treated within first-principles frameworks, a quantitative description of EMC, especially for transport, remains elusive because of the lack of theoretical formalism. Consequently, even for elemental iron, EPC-only calculations miss both the magnitude and the $T^2$ component of resistivity. This discrepancy has long been attributed to EMC, although direct computational evidence has been lacking and the underlying transport mechanism remains unresolved. Here we develop a unified first-principles formalism for EMC in collinear magnetic systems within many-body perturbation theory, complemented by machine-learning spinful Hamiltonians that supply quantities not directly accessible from conventional first-principles methods. Our framework enables ab initio transport calculations including EMC effects for the first time. Applied to ferromagnetic $\alpha$-Fe, our approach yields electron spectral functions consistent with previous studies. More importantly, we recover the full $T^2$ component of resistivity with a coefficient in quantitative agreement with measurement and reveal that the $T^2$ component cannot be attributed solely to EMC, as has long been assumed, but is dominated by the strong EPC-EMC interplay. Extending to antiferromagnetic K-doped $\mathrm{BaMn_2As_2}$, our method captures the ARPES-observed magnon-induced kink and a large EMC strength of $\sim 3$ comparable to experimental measurements, demonstrating the generality of the framework. Our work closes a longstanding gap in the quantitative understanding of transport in magnetic systems and provides a predictive foundation for examining magnon-mediated phenomena.

physics.comp-ph↗
arXiv2026

Dimensional crossover and local strain induced deflection of the spin spiral state in multiferroic NiI2

Low-dimensional multiferroics hold great promise for integrated magnetoelectric devices. Spin spiral state has recently been shown to induce ferroelectricity in single-layer van der Waals (vdW) material NiI2. However, how this state evolves and can be tuned towards the two-dimensional limit remain unclear. Here, we combine spin-polarized scanning tunneling microscopy, layer-by-layer film growth, and multi-scale theoretical modeling to investigate the spin spirals in NiI2 thin films. As the film thickness increases from 1 to 7 monolayers, we observed a continuous increase of spin-spiral wavelength and a rotation of wavevector from near [110] to [1-10] direction, which evidences a dimensional crossover primarily driven by enhanced interlayer exchange energy. Moreover, we find that the film wrinkles can cause deflection of the spin spiral wavevector, which is caused by local curvature induced modification of exchange interactions. Our findings establish thickness and local strain as two tuning methods for engineering non-collinear helical magnetism and accompanied electric polarization in vdW multiferroics.

cond-mat.mes-hall↗
arXiv2026

Nonadiabatic Molecular Dynamics on Real-time Excited-State Surfaces via Machine Learning Hamiltonians

Simulating the coupled, nonequilibrium dynamics of electrons and nuclei is a central challenge in chemistry, physics, and materials science, governing phenomena from photocatalysis to quantum information. The primary bottleneck has been the lack of a general, accurate, and efficient method for modeling the complete excited-state landscape: the potential energy surfaces, forces, and non-adiabatic couplings for multiple electronic states. While machine learning has revolutionized ground-state simulations and shown promise for excited states in molecules, a unified framework that solves the complete multi-state problem for general condensed matter systems has remained elusive. Here we introduce on-the-fly N${^2}$AMD (Neural network NAMD), a machine learning framework that makes on-the-fly NAMD in solids a reality. By employing an equivariant neural network to predict the system Hamiltonian, the framework delivers excited-state energies, forces, and non-adiabatic coupling vectors at a fraction of the cost of ab initio calculations. Crucially, it allows simulations with hybrid functional accuracy, a level of approach previously inaccessible for NAMD. We showcase its capabilities with three topical examples: correcting order-of-magnitude errors in carrier dynamics predicted by conventional procedure in a MoS$_2$/WS$_2$ heterostructure, simulating previously inaccessible photoinduced ferroelectric switching, and capturing real-time polaron formation in TiO$_2$ at the hybrid-functional level. On-the-fly N${^2}$AMD moves beyond the limitations of equilibrium theory, establishing a new paradigm for the predictive, first-principles design of materials operating far from equilibrium.

physics.comp-ph↗
arXiv2026

Switchable Altermagnetism via Spin-Induced Improper Polarization

Enabling reversible spin-splitting switching in stray-field-free altermagnets is promising for spintronic applications, but currently limited to a narrow class of polar materials. We propose a broader approach based on spin-induced improper polarization in nonpolar dual-sublattice magnets. We demonstrate this mechanism in DyFeO3, where the product of nonpolar Fe and Dy spin modes transforms as an induced polar mode. Density functional theory shows that the relative Dy--Fe spin alignment selects the polarization, while the Fe sublattice controls nonrelativistic spin splitting, thus enabling reversible switching. These results establish spin-induced improper polarization as a route to switchable altermagnetism in nonpolar bulk systems.

cond-mat.mtrl-sci↗
arXiv2026

Rethinking Scientific Discovery in the Agentic Era

Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate problem formulation, literature grounding, model use, simulation, validation, and knowledge reuse. This paper presents \textbf{SCION (Scientific Collaborative Innovation with Agentic Organizational Nexus)}, an agentic scientific operating system that acts as an \textbf{organizational nexus}. Through a Science Agent serving as a \textbf{Meta-Harness}, SCION connects scientific tasks, tools, agents, artifacts, and memory, transforming research into an executable, auditable, and reusable operational process. At its core is the \textbf{Research Execution Plan (REP)}, which compiles high-level scientific intent into staged objectives, dependencies, verification checkpoints, tool requirements, expected artifacts, and fallback conditions. SCION further integrates hierarchical multi-agent execution, profile-driven specialization, selective context construction, governed delegation, and layered epistemic memory to support long-horizon scientific work. We formulate discovery under SCION as \textbf{Target-conditioned Inverse Search} and extend it to hidden-target settings through batch active search under finite experimental budgets. Applications in materials analysis, molecule design, and protein or antibody screening, together with experiments on scientific reading, idea generation, molecule generation, and antibody screening, show that SCION outperforms existing autonomous research-agent baselines, especially in decomposition, verification, refinement, and memory reuse. Overall, SCION shifts AI from isolated tools toward a coordinated operational layer for traceable and reusable scientific innovation.

cs.CL↗
arXiv2026

Latent Genetic Algorithm for Crystal Structure Prediction

Predicting crystal structures requires navigating rugged energy landscapes in which favorable local motifs must be inherited across candidates with incompatible cells, densities, and symmetries. Conventional real-space crossover often destroys these motifs when parent structures are geometrically mismatched. Here we show that latent representations learned by pretrained universal interatomic potentials can serve as continuous evolutionary coordinates for crystal structure prediction. In the Latent Genetic Algorithm (LGA), offspring are generated by inverse optimization of atomic positions and lattice vectors to match a target latent representation, which is constructed via interpolation of the parent latent vectors. LGA suppresses high-energy and short-contact offspring, increases the HfO$_2$ ground-state recovery rate from 20-35% to 60-95%, and enables a unified variable-supercell search over 16 perovskites with a nearly tenfold reduction in search cost. Applied to (PbTiO$_3$)$_n$/(PbZrO$_3$)$_n$ superlattices, LGA reveals $\sqrt{2} \times 3\sqrt{2} \times 1$ long-period ground-state structures characterized by a common in-plane finite-$q$ modulation $q{_\parallel} = (1/6,1/6)$ and layer-coupled sidebands. To our knowledge, this in-plane periodicity has not been reported in any related oxide perovskite superlattice studies. Altogether, LGA offers a powerful representation-guided paradigm for ground-state structure prediction and provides a practical, decoder-free route toward materials inverse design.

physics.comp-ph↗
arXiv2026

General Theory for Ferroelectric Control of Spin Splitting in Collinear Antiferromagnets

Electrical control of magnetism is crucial for next-generation spintronics. While recent advances have demonstrated ferroelectric switching in two-dimensional magnets, a general design strategy spanning different dimensionalities remains elusive. Here, we develop a group-theoretical framework for achieving ferroelectric control of spin splitting in collinear antiferromagnets, including altermagnets and compensated ferrimagnets. By systematically classifying switching operators through symmetry analysis, we identify a universal pathway for the simultaneous reversal of electric polarization and nonrelativistic spin splitting.We validate this approach in three representative systems: quasi-one-dimensional $(6,14)$ Zigzag graphene nanoribbons, two-dimensional~\ch{Nb3I8}, and three-dimensional altermagnetic~\ch{MnSe2}. Our work establishes a versatile design paradigm for magnetoelectric devices and expands the functional landscape of low-power spintronic materials beyond the low-dimensional limit.

cond-mat.mtrl-sci↗
arXiv2026

Unified definition of ferroelectricity

Recent theoretical and experimental advances in quantum ferroelectrics suggest that ferroelectricity can also emerge in non-polar space group, highlighting the limitations of conventional polar space group criteria in identifying ferroelectric materials. Here, we introduce a unified definition based on switchable polarization differences between energetically equivalent states, which naturally encompasses conventional and quantum ferroelectrics. Guided by this principle, we implement a high-throughput screening strategy that systematically identifies both conventional and quantum ferroelectrics among experimentally synthesized materials. In particular, we identify a new type of quantum ferroelectric in which the quantized polarization arises from arbitrary ionic displacements, in contrast to previous quantum ferroelectrics (including both fractional and integer quantum ferroelectrics) where quantized polarization results from fractional or integer ionic displacements. Notably, we find that materials such as Ba3I6 and Cs2PdC2 exhibit low switching barriers and robust insulating behavior, highlighting their experimental viability. Our results reconcile conventional and quantum ferroelectrics, expand the accessible materials landscape, and provide a practical roadmap for discovering next-generation ferroelectrics with advanced switchable functionalities.

cond-mat.mtrl-sci↗
arXiv2026

Tangent-Plane Evidential Uncertainty in Active Learning for Magnetic Interatomic Potentials

Magnetic interatomic potentials need to account for coupled lattice and spin degrees of freedom, yet constructing reliable training sets remains costly because noncollinear first-principles labels are expensive. Active learning can mitigate this cost, provided that the uncertainty estimate is physically meaningful for the magnetic-response targets that drive spin reorientation. Here we extend the $\mathrm{e}^2\mathrm{IP}$ evidential framework to magnetic machine-learning interatomic potentials by formulating the projected spin-force likelihood and the corresponding epistemic uncertainty in the tangent plane orthogonal to the local spin direction. This construction prevents the uncertainty model from allocating probability mass to a radial spin component that is absent from the constrained-moment supervision. Using bulk BiFeO$_3$ and monolayer CrTe$_2$ as benchmark systems, we show that the resulting tangent-plane epistemic uncertainty indicator $U_{\mathrm{epi}}^{\mathrm{sf}}$ correlates strongly with prediction error and selects more informative configurations than random sampling, simultaneously improving energy, force, and projected spin-force accuracy. These results demonstrate a physically interpretable and data-efficient route for constructing uncertainty-aware magnetic machine-learning interatomic potentials.

physics.comp-ph↗
arXiv2026

A Fully Ab-Initio Spin-Lattice Dynamics Framework for Magnetic Materials

Coupled spin-lattice dynamics (SLD) underlie a wide range of magnetic phenomena, yet a unified first-principles framework that propagates both degrees of freedom without empirical parameterization has remained elusive. We present a fully ab initio SLD approach integrated into VASP, in which interatomic forces and effective magnetic fields are obtained at each time step from self-consistent constrained-moment density-functional calculations. The method is validated on four materials spanning ferromagnetic, non-collinear, and geometrically frustrated orders, recovering the correct magnetic ground state in every case from random initial conditions. SLD trajectories also provide physically correlated training data for magnetic machine-learning potentials, as demonstrated for BiFeO$_3$ by a reduction of up to approximately one order of magnitude in energy MAE over training on randomized spin configurations. This framework opens a practical first-principles route to finite-temperature spin-lattice coupled phenomena in magnetic materials.

cond-mat.mtrl-sci↗
arXiv2026

Machine learning Hamiltonian enables scalable and accurate defect calculations: The case of oxygen vacancies in amorphous SiO$_2$

Point defects critically influence the properties of materials and devices, yet density functional theory (DFT) remains computationally demanding for defect supercell calculations. Machine learning interatomic potentials (MLIPs) offer high efficiency but require extensive datasets. MLIPs trained only on defect configurations in small supercells exhibit systematic energy errors in larger supercells, demonstrating limited transferability. Here, we present a machine learning Hamiltonian (MLH) model-based method for calculating total energies and atomic forces in defect supercells with linear-scaling computational cost, enabling efficient structural relaxation and accurate formation energy predictions. We take oxygen vacancies in amorphous SiO$_2$ as an example and train the MLH model on defect configurations in 95-atom supercells, with the training data derived from 120 self-consistent field calculations and 12 structural relaxations. The MLH model enables efficient structural relaxations for host (defect-free) and defect systems in larger supercells, avoiding the systematic energy errors observed in MLIPs. The cancellation of energy errors between host and defect systems yields accurate formation energy predictions, with deviations from DFT below 50 meV. The proposed method holds significant potential for defect simulations in complex materials.

cond-mat.mtrl-sci↗
arXiv2026

Microscopic evidence of spin-driven multiferroicity and topological spin textures in monolayer NiI2

In type II multiferroics, noncollinear spin textures are expected to induce electric polarization directly, leading to strong magnetoelectric coupling. Realizing such spin driven multiferroicity in two-dimensional systems, and elucidating the interplay between local spins and electric polarization, are of both fundamental and technological importance. Here, using vectorial spin polarized scanning tunneling microscopy, we investigated the spin-driven multiferroicity in monolayer NiI2 at atomic scale. We identify a canted spin-spiral state with fully determined spin rotation plane, accompanied by a 2Q charge modulation. At spin spiral domain walls, we discover topological spin textures that composed of meron/antimeron pairs. These textures are associated with distinct charge pattern and notable band shifts, indicating local bound charges induced by variations of ferroelectricity at domain wall. Our observations are well captured by a realistic spin model incorporating Kitaev interactions and generalized spin-current model of type II multiferroicity. The findings provide microscopic evidence of spin-driven multiferroicity in an extreme 2D system and establish a platform for low-dissipation, electric-field control of topological spin textures.

cond-mat.mes-hall↗
arXiv2026

Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians

Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory, yet current models omit long-range Coulomb interactions that govern physics in polar crystals and heterostructures. We derive closed-form long-range Hamiltonian matrix elements in a nonorthogonal atomic-orbital basis through variational decomposition of the electrostatic energy, deriving a variationally consistent mapping from the electron density matrix to effective atomic charges. We implement this framework in HamGNN-LR, a dual-channel architecture combining E(3)-equivariant message passing with reciprocal-space Ewald summation. Benchmarks demonstrate that physics-based long-range corrections are essential: purely data-driven attention mechanisms fail to capture macroscopic electrostatic potentials. Benchmarks on polar ZnO slabs, CdSe/ZnS heterostructures, and GaN/AlN superlattices show two- to threefold error reductions and robust transferability to systems far beyond training sizes, eliminating the characteristic staircase artifacts that plague short-range models in the presence of built-in electric fields.

physics.comp-ph↗
arXiv2026

Topological-transition-driven Giant Enhancement of Second-harmonic Generation in Ferroelectric Bismuth Monolayer

The interplay between band topology and light in condensed materials could unlock intriguing nonlinear optical phenomena, enabling modern photonic technologies such as quantum light sources and sub-wavelength topological lasers. Here, we unveil that a buckling-tuned topological transition in ferroelectric bismuth monolayer unleashes a giant second-harmonic generation. Using first-principles calculations, we surprisingly find that ferroelectric bismuth monolayer with a buckling parameter, $\Delta h$, has a large susceptibility $\chi^{(2)}$ on the order of $10^{7}$ $\mathrm{pm}^2/\mathrm{V}$, exceeding monolayer MoS$_2$ by about two orders of magnitude. When $\Delta h$ is engineered to the critical window where Dirac electrons emerge, a low-frequency resonance appears, boosting $\chi^{(2)}$ by an additional order of magnitude. We show that this enhancement is localized on the Dirac cones and dominated by intraband modification contributions. Based on an extended Dirac model, we establish that this enhancement physically originates from the ultralight effective masses $m^{*}$ of Dirac electrons through scaling with the Fermi velocity $v_F$ and band gap $E_g$. Our findings provide a general paradigm for achieving exceptional second-harmonic generation via engineering topological criticality, and could serve as an experimental signature of Dirac electrons in topological materials.

cond-mat.mtrl-sci↗
arXiv2025

Design and theory of switchable linear magnetoelectricity by ferroelectricity in Type-I multiferroics

We present a comprehensive theoretical investigation of magnetoelectric (ME) coupling mechanisms in 19 altermagnetic and 4 ferrimagnetic Type-I multiferroics using electronic band structure calculations with spin-orbit coupling, a first-principles ME response framework, and spin-space-group theory analysis. We formulate a universal scheme for realizing nonvolatile ME coupling in Type-I multiferroics, where two distinct pathways emerge, each dictated by spin-space symmetry. The first pathway is associated with switching of the spin splitting or the now familiar spin-momentum locking in reciprocal space, characteristic of some altermagnetic mul-tiferroics that exhibit coexisting antiferromagnetism and ferroelectricity. The second pathway involves real-space magnetization switching via electric polarization reversal, characterized by switchable components of the linear ME tensor, despite the traditionally weak coupling in Type-I systems due to the independent origins of magnetism and ferroelectricity. We demonstrate that these two intrinsic ME coupling mechanisms are mutually exclusive and propose thermodynami-cally stable compounds for experimentation. Our findings establish general design principles for controlling robust nonvolatile ME effects in multiferroic materials.

cond-mat.mtrl-sci↗
arXiv2025

Fractional Quantum Multiferroics from Coupling of Fractional Quantum Ferroelectricity and Altermagnetism

Multiferroics, which combine ferroelectric and magnetic order, offer a transformative platform for next-generation electronic devices. However, the intrinsic competition between the mechanisms driving ferroelectricity and magnetism in single-phase materials severely limits their performance, typically resulting in weak magnetoelectric coupling at room temperature. Here, we propose a solution to this long-standing challenge through the novel concept of fractional quantum multiferroics (FQMF), where strong magnetoelectric coupling is naturally realized by coupling fractional quantum ferroelectricity (FQFE) with altermagnetism (AM). Symmetry analysis shows that reversing the FQFE polarization necessarily inverts the AM spin splitting under parity-time ($\mathcal{PT}$) or time-reversal ($\mathcal{T}\tau$) operations. A minimal tight-binding model reproduces this effect, demonstrating electrically driven spin control without rotating the N\'eel vector. First-principles calculations further identify a broad family of candidate materials in two and three dimensions including bulk MnTe, Cr$_2$S$_3$, Mn$_4$Bi$_3$NO$_{15}$ and two-dimensional AB$_2$ bilayers such as MnX$_2$ (X=Cl, Br, I), CoCl$_2$, CoBr$_2$, and FeI$_2$. Notably, MnTe exhibits a high N\'eel temperature ($\sim$300 K) and a large electrically switchable spin splitting ($\sim$0.8 eV), demonstrating room-temperature magnetoelectric performance that surpasses that of conventional multiferroics. To further showcase the technological potential, we propose an electric-field-controlled FQMF tunnel junction based on MnTe that achieves tunneling magnetoresistance exceeding 300\%. This work establishes FQMF as a distinct and promising route to achieving room-temperature strong magnetoelectric coupling, opening a new avenue for voltage-controlled spintronics.

cond-mat.mtrl-sci↗