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Zhong-Yi Lu

Publications and source records attributed to Zhong-Yi Lu.

At least 19 recordsLinked to original sources
arXiv2026

Microscopic Origin of Pressure-Enhanced and Robust Superconductivity in Infinite-Layer La$_{0.8}$Sr$_{0.2}$NiO$_2$

Recent transport measurements on freestanding La$_{0.8}$Sr$_{0.2}$NiO$_2$ membranes revealed a broad superconducting dome extending from ambient pressure to 210 GPa, with an onset transition temperature reaching 74.5 K near 146 GPa. Using first-principles calculations, a pressure-dependent two-orbital model, and self-consistent FLEX calculations combined with the linearized Eliashberg equation, we determine how compression modifies the pairing tendency. Pressure increases the kinetic-energy scale, reduces $U_x/t_1$, strengthens interlayer hybridization, and transfers holes from the La/Sr-derived charge reservoir to the correlated Ni sector. Within the present low-energy description, the increasing kinetic scale and the approach to optimal intermediate coupling account for the initial enhancement of pairing, whereas pressure-induced self-doping into the overdoped regime is primarily responsible for its high-pressure suppression. Despite a pronounced three-dimensionalization of the Fermi surface, the pairing-relevant spin susceptibility remains weakly dependent on $q_z$ and peaked near $(π,π)$. Consequently, the Ni-$d_{x^2-y^2}$-dominated $d$-wave pairing state remains stable over the calculated pressure range. These results provide a unified microscopic interpretation of both the superconducting dome and its unusual robustness under megabar compression.

cond-mat.supr-con↗
arXiv2026

Symmetry-Preserving Phase Transitions in $AM_2$Al$_9$ Materials under Pressure

External parameters such as temperature, pressure, and chemical doping can induce structural phase transitions in materials. Although such transitions usually involve a change in symmetry, an uncommon exception is the isostructural phase transition, which is first order yet preserves the symmetry of the parent structure. Using first-principles calculations, we show that $AM_2$Al$_9$ compounds ($A$ = Ba, Ca, Sr, or Eu; $M$ = Fe, Co, or Ni) undergo pressure-induced isostructural phase transitions. At the transition pressure, these systems exhibit a pronounced volume collapse while retaining the same crystal symmetry and space group ($P6/mmm$). Bonding analysis based on the integrated crystal orbital Hamilton population (ICOHP) shows that the transition is driven by a redistribution of bonding character between intralayer and interlayer atomic bonds. Because isostructural transitions are rare in single crystals, $AM_2$Al$_9$ provides a promising platform for investigating critical phenomena under pressure and for deepening our understanding of symmetry-preserving structural transitions.

cond-mat.mtrl-sci↗
arXiv2026

Strain-driven orbital-selective reconstruction and bicollinear-to-stripe evolution in FeTe

FeTe, as a representative parent material among iron-based superconductors, provides an ideal platform for exploring the interplay among orbital-selective correlations, magnetism, and unconventional superconductivity. However, a unified picture of the correlated electronic structure and magnetism of FeTe under strain remains to be fully clarified. Here, combining density functional theory plus dynamical mean-field theory and Heisenberg model analysis, we uncover an orbital-selective reconstruction of the correlated electronic structure and reveal a strain-driven trajectory from bicollinear to stripe antiferromagnetism (AFM) via an intermediate competing staggered $n$-mer AFM regime in FeTe. Moderate strain gives rise to a regime where more coherent quasiparticles coexist with suppressed local moments. Further strain drives FeTe into an incoherent correlated regime with robust local moments and Fe-$3d_{z^2}$-dominated low-energy states. These results establish a strain-driven trajectory across distinct magnetic and correlated electronic states in FeTe.

cond-mat.supr-con↗
arXiv2026

Stacking-induced type-II quantum spin Hall insulators with high spin Chern number in unconventional magnetism

While stacking two type-I quantum spin Hall insulators typically results in a trivial insulator, the behavior of the assembly of type-II quantum spin Hall insulators remains unexplored. In this article, based on calculations of a lattice model, we demonstrate that stacking two type-II quantum spin Hall insulators does not yield a trivial insulator but instead forms a nontrivial quantum spin Hall insulator with high spin Chern number. In this phase, two pairs of topological edge states with opposite chirality and polarization coexist at the boundary. Our calculations further reveal that the quantized spin Hall conductivity of the bilayer is twice that of the monolayer. When U(1) symmetry is present, the high spin Chern number phase remains stable; when U(1) symmetry is broken, it persists over a broad parameter range. Furthermore, based on first-principles electronic structure calculations, we demonstrate that bilayer Nb$_2$SeTeO is a type-II quantum spin Hall insulator with a high spin Chern number in altermagnetism and unconventional compensated magnetism. Moreover, extending this strategy to multilayer stacks naturally leads to a quantum spin Hall insulator with a higher spin Chern number. Our work not only deepens the distinction between type-I and type-II quantum spin Hall insulators, but also offers a route toward realizing highly quantized spin Hall conductivity.

cond-mat.mes-hall↗
arXiv2026

Bipolarized Weyl semimetals and quantum crystal valley Hall effect in two-dimensional altermagnetic materials

Magnetism and topology are two major areas of condensed matter physics. The combination of magnetism and topology gives rise to more novel physical effects, which have attracted strongly theoretical and experimental attention. Recently, the concept of altermagnetism has been introduced, characterized by a dual nature: real-space antiparallel spins with zero total magnetic moment and reciprocal-space anisotropic spin polarization. The amalgamation of altermagnetism with topology may lead to the emergence of previously unobserved topological phases and the associated physical effects. In this study, utilizing a four-band lattice model that incorporates altermagnetism and spin group symmetry, we demonstrate that type-I, type-II, and type-III bipolarized Weyl semimetals can exist in altermagnetic systems. Through the first-principles electronic structure calculations, we predict four ideal two-dimensional type-I altermagnetic bipolarized Weyl semimetals Fe$_2$WTe$_4$ and Fe$_2$MoZ$_4$ (Z=S, Se, Te). More significantly, we introduce the quantum crystal valley Hall effect, a phenomenon achievable in three of these materials namely Fe$_2$WTe$_4$, Fe$_2$MoS$_4$, and Fe$_2$MoSe$_4$, when spin-orbit coupling is considered. Therefore, our work not only enriches the topological phases but also provides a material platform for studying novel topological phases in altermagnetism.

cond-mat.mtrl-sci↗
arXiv2026

Local Spin Excitations Mediate Quasiparticle Breakdown in the Orbital-Selective Mott Phase

The orbital-selective Mott phase (OSMP) is commonly described as a coexistence of localized and itinerant electrons within effectively decoupled orbitals, but emerging evidence for quasiparticle breakdown points to physics beyond this picture, whose microscopic origin remains unknown. Using dynamical mean-field theory for the two-band Hubbard model, we show that the spin-flip and Ising-type components of Hund's coupling generate local spin excitations (LSEs). These LSEs couple electrons between different orbitals, renormalize quasiparticle lifetimes and binding energies, and thereby destroy well-defined quasiparticles in the OSMP. Removing these two components of Hund's coupling restores coherent quasiparticle behavior and fully decouples the charge dynamics of the two bands. Our results therefore identify electronic coupling to LSEs as the fundamental mechanism driving quasiparticle breakdown within the OSMP.

cond-mat.str-el↗
arXiv2026

Screening phonon-mediated superconductors from static orbital Hamiltonians

The first-principles search for superconductors is severely limited by the high cost of electron-phonon coupling (EPC) calculations. Here we develop a low-cost, physically transparent framework that identifies strong-EPC materials directly from static orbital-based Hamiltonians without explicit phonon perturbation calculations. Verification using density functional perturbation theory (DFPT) for representative superconductors shows that the framework captures semi-quantitatively the EPC scale at substantially lower computational cost. Applied to more than 36,000 compounds in the MattKeyBond database, it identifies 34 dynamically stable superconducting candidates with calculated $T_c > 10$ K after DFPT verification. These candidates reveal two distinct routes to relatively high-$T_c$ superconductivity: a metallized covalent $σ$-bond route that is more favorable for achieving high-$T_c$ superconductors, and a Fermi-level density-of-states accumulation route that can enhance $T_c$ but usually to a more limited extent.

cond-mat.supr-con↗
arXiv2026

Twist-induced magnetic topological phase transition in stacked altermagnetic CrO

Interlayer twisting offers a geometric route to controlling electronic states, but whether it can simultaneously reconstruct magnetic symmetry and band topology remains unclear. Here, based on symmetry analysis and first-principles calculations, we show that commensurate twisting drives magnetic topological phase transitions in stacked bilayer CrO. In particular, it transforms an antiferromagnetic Dirac semimetal into either a $d$-wave altermagnetic bipolarized Weyl semimetal or an unconventional compensated magnetic Weyl semimetal. A key result is that the Weyl points in the $d$-wave altermagnetic phase lie at generic $k$ points in the Brillouin zone and are protected by the spin symmetry $\left\{ C_2 T||C_{2z} T\right\}$. This sharply contrasts with conventional two-dimensional Weyl semimetals, where Weyl points are typically protected by mirror or rotational symmetries and thus pinned to high-symmetry lines. We further show that commensurate twisting preserves the spin symmetry $\left\{ C_2 T||C_{2z} T\right\}$, making the Weyl phase a robust consequence of twisting rather than a fine-tuned feature of a specific angle. Our work establishes a symmetry-based route to engineering magnetic topological phases in twisted two-dimensional materials.

cond-mat.mtrl-sci↗
arXiv2026

Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors

Although chemical bonding is the fundamental mechanistic bridge connecting atomic structure to macroscopic material properties, current data-driven materials science largely treats it as an implicit "black box". Existing machine learning (ML) models rely predominantly on geometric coordinates, forcing them to implicitly relearn complex quantum mechanics from scratch. This lack of intermediate physical features limits model interpretability and generalizability, particularly when training data is scarce. To solve this problem, we introduce MattKeyBond, a bond-centric materials database that explicitly maps the local electronic landscape and bonding interactions of materials. Building on this, we propose Bonding Attractivity (BA), a novel element-specific descriptor that quantifies the intrinsic capability of atoms to form covalent networks. By providing pre-calculated, energy-dimensional bonding descriptors, MattKeyBond transforms the implicit "black box" into physically interpretable features. This strategy relieves ML models from the burden of deducing physical laws from pure geometry, enabling accurate predictions even with limited data and seamlessly integrating electronic structure theory into modern AI workflows.

cond-mat.mtrl-sci↗
arXiv2026

Optimization dynamics of Transformer backflow neural quantum states for the two-dimensional Hubbard model

Building on the multi-determinant Transformer backflow neural quantum state (NQS) ansatz and the associated multi-stage training workflow for the doped two-dimensional Hubbard model, we investigate how the optimization dynamics of the NQS depend on several key optimization and architectural hyperparameters. The workflow consists of neural-network backflow (NNB) initialization, supervised Transformer pre-training, and main energy optimization using the Moment-Adaptive ReConfiguration Heuristic (MARCH) within variational Monte Carlo. Using the doped $4\times4$ periodic Hubbard model at $U=8$ as a baseline, we examine how the update-norm threshold, Transformer width, number of determinant channels, and Monte Carlo batch size affect convergence. We find that a moderate update constraint improves the efficiency of MARCH optimization, larger Transformer width and more determinant channels improve the expressive capacity of the ansatz, and larger Monte Carlo batches reduce sampling noise in the update direction. We further test the same workflow at half filling, weaker interaction strength, open boundary conditions, and on a larger $8\times8$ doped lattice. These results identify practical optimization trends for Transformer backflow NQSs and highlight the balance between ansatz expressivity, MARCH update stability, and Monte Carlo sampling quality.

cond-mat.str-el↗
arXiv2026

Emergent d-wave altermagnetism in chlorine-adsorbed FeSe monolayer

The recent emergence of altermagnetism has opened new frontiers in condensed matter physics, yet material platforms capable of hosting both intrinsic altermagnetic order and superconductivity remain exceedingly rare. Here, based on symmetry analysis and first-principles calculations, we propose a realistic route to engineer robust altermagnetism in monolayer FeSe, a prototypical iron-based superconductor. By designing a stoichiometric Fe2Se2Cl structure through single-side Cl adsorption and introducing gate-tunable hole doping, we achieve a highly stable altermagnetic ground state. Our calculations reveal a synergistic mechanism: hole doping firmly stabilizes the checkerboard magnetic order, while the asymmetric ligand environment intrinsically breaks the outof-plane spatial inversion symmetry. Consequently, this interplay induces a giant altermagnetic spin splitting of up to 620 meV. Crucially, we demonstrate that this altermagnetic state and its giant spin splitting are highly resilient, persisting even in a 10-layer slab model that accurately simulates the bulk limit. By introducing altermagnetism into the well-established FeSe-based superconducting family, our findings identify Fe2Se2Cl as a promising platform for spintronic applications and motivate future studies of the possible interplay between altermagnetism and superconductivity.

cond-mat.mtrl-sci↗
arXiv2026

$\texttt{iNORG}$: An open-source quantum impurity solver package based on the natural orbitals renormalization group

In the context of dynamical mean-field theory (DMFT) calculations for strongly correlated electron systems, quantum impurity solvers play a central computational role in treating correlated lattice models and realistic materials. Consequently, developing efficient and robust quantum impurity solvers remains a key challenge. In this paper, we present an open-source quantum impurity solver package based on the natural orbitals renormalization group (NORG) method, dubbed $\texttt{iNORG}$. This software delivers high accuracy with reduced computational cost by optimizing the bath representation using natural orbitals and incorporating advanced features such as efficient Hilbert space selection and efficient algorithms for computing Green's functions. We first introduce the basic principle of the NORG method and then discuss the implementation details. The software framework, major features, and installation procedure for $\texttt{iNORG}$ are explained as well. Finally, several simple examples are presented to demonstrate the usage of $\texttt{iNORG}$.

cond-mat.str-el↗
arXiv2026

Layer-resolved Electronic Structure and Correlation of Low-$n$ Square-planar Nickelates: A DFT+DMFT Prediction of Superconducting Candidates

Multi-layer square-planar nickelates provide a rare platform in which the nominal Ni valence, dimensionality, and layer-resolved electronic structure can be tuned within the same structural family. Recent experiments have found superconductivity in $n=4$--8 $R_{n+1}Ni_nO_{2n+2}$ compounds, with the highest $T_c$ near $n=6$, whereas the more heavily hole-doped $n=3$ member remains nonsuperconducting. Here we propose spacer-layer Cl doping as a route to convert low-$n$ nickelates into superconducting candidates. Compared with changing the layer number $n$, Cl substitution on the spacer-layer oxygen sites offers a chemically natural way to continuously tune the Ni valence while leaving the NiO$_2$ planes largely intact; the lower-$n$ compounds may also be more accessible for synthesis. Using density functional theory combined with dynamical mean-field theory, we show that electron-compensated $n=2$ and $n=3$ La-based nickelates, targeted to the nominal Ni valence of superconducting $n=6$, develop Ni-$d$ correlations comparable to those of superconducting higher-$n$ compounds while preserving the characteristic low-energy Ni-$d$ electronic structure. These results suggest spacer-layer Cl doping as a promising strategy for designing low-$n$ square-planar nickelate superconductors.

cond-mat.supr-con↗
arXiv2026

PhononScore: a phonon-aware scoring function for dynamical stability

In recent years, crystal generation models have enabled the design of massive numbers of candidate materials. However, the lack of dynamical stability among generated structures has become a major bottleneck preventing their translation into practical materials discovery. To address this challenge, we propose PhononScore, a phonon-aware scoring function for crystal generation. Unlike computationally expensive explicit phonon calculations, PhononScore predicts a unified stability score from crystal structures, enabling ranking of candidate materials dynamical stability with second-level computational cost. We construct a multi-fidelity phonon dataset containing 157,463 crystal structures. On the PhononBench benchmark, PhononScore improves the average dynamical stability rate of candidate pools generated by nine crystal generation models from 30.7% to 83.7%, achieving a 2.72-fold enrichment of stable structures, while the average stability rate of the Top-10 candidates reaches 97.5%. On a high-fidelity DFT-PBE phonon benchmark, the DFT-finetuned PhononScore-DFT increases the Top-100 stability rate to 93.0% and achieves 5-6-fold enrichment of dynamically stable structures under an extremely imbalanced hard-screening scenario. As a materials-screening tool analogous to scoring functions in drug discovery, PhononScore can serve directly as a dynamical-stability feedback signal for crystal generation, active learning, and reinforcement learning, enabling second-level stability-aware reranking without explicit phonon calculations and providing a unified and efficient dynamical stability evaluator for high-throughput materials discovery, active learning, reinforcement learning, and closed-loop inverse design. The online PhononScore platform is available at: http://phononbench.cn/phononscore/

cond-mat.mtrl-sci↗
arXiv2026

NQS-Agent: Health-Aware Agentic Hyperparameter Optimization for Neural-Network Quantum States

Neural-network quantum states (NQS) provide expressive variational representations for strongly correlated quantum many-body systems, but their practical accuracy depends sensitively on architecture-level hyperparameters and optimization schedules. Here we develop NQS-Agent, an implemented open-source software framework for health-aware hyperparameter optimization (HPO) in NQS calculations. Its workflow monitors energy trajectories, detects destructive optimization events, stops unstable calculations, modifies the learning-rate schedule, resumes optimization from safe checkpoints, and ranks candidates with an anomaly-aware score. We demonstrate the approach on a residual convolutional NQS for the square-lattice Heisenberg $J_1$-$J_2$ model, using architectures with parameter counts comparable to aCNN, a convolutional NQS architecture used here as a reference. The results show that NQS-Agent improves over the reported human-tuned aCNN baseline for the aCNN reference architecture and identifies a structurally distinct wide-and-shallow competitive candidate within the parameter-count-matched residual-CNN search space. These results show that the stability and recovery history of an optimization trajectory should be considered when assessing an NQS result. Health-aware HPO therefore provides a reproducible tuning protocol that goes beyond selecting a single lowest-energy calculation.

cond-mat.str-el↗
arXiv2026

AI-accelerated metallized $σ$-bonding screening for superconductor discovery

The computational discovery of phonon-mediated superconductors is hindered by the prohibitive cost of density functional perturbation theory (DFPT). Here, guided by the metallized $σ$-bonding picture, we introduce the $σ$-bonding density of states ($σ$DOS) as an efficient physical descriptor to identify high-transition-temperature ($T_{\mathrm{c}}$) superconductors from density functional theory (DFT)-level electronic structure without explicit DFPT calculations. The evaluation of $σ$DOS can be further accelerated by a deep-learning DFT Hamiltonian method, enabling efficient large-scale screening for superconductors. Screening 2 million materials, we identify B$_{13}$Se as an ambient-pressure superconductor candidate with predicted $T_{\mathrm{c}} > 40$~K, together with a family of high-$T_{\mathrm{c}}$ B$_{13}X$ candidates, supporting the effectiveness of this discovery strategy. By bridging physics priors with AI acceleration, this study delivers an efficient and generalizable route for computational materials discovery in the AI era.

physics.comp-ph↗
arXiv2026

Ultrafast optical route to coupled ferroelectric and altermagnetic switching

Exploring novel magnetoelectric coupling mechanisms to achieve control of ferroelectric polarization and magnetism is highly significant for both fundamental science and electronic device applications. Although extensive studies have been conducted on electrical switching of magnetism in multiferroic materials, simultaneous ultrafast laser switching of ferroelectric polarization and altermagnetism remains unexplored. In this letter, we propose that the ultrafast laser can be used to switch ferroelectric polarization and altermagnetism concurrently in charge-order-induced altermagnetic ferroelectrics. Building on this idea, we further demonstrate that such dual switching can be realized in charge-order-induced altermagnetic ferroelectric LiV$_2$F$_6$ by symmetry analysis and time-dependent density functional theory (TDDFT) calculation. Given that LiV$_2$F$_6$ has already been experimentally synthesized, our work not only provides an ideal material platform for experimentally realizing simultaneous switching of ferroelectric polarization and altermagnetism but also holds potential application value in future ultrafast spintronic devices.

cond-mat.mtrl-sci↗
arXiv2026

InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery

Inverse materials design starts from target functionality and searches for structures that can realize it. Its value in closed-loop discovery depends not only on prediction performance, but also on whether expensive first-principles results are independently validated, provenance-recorded, and admitted as feedback only when evidence is sufficient. This is especially important for composite properties such as carrier mobility, where a final scalar value hides intermediate quantities, fit quality, convergence history, and workflow assumptions. Here we present InvDesMobility, a reliability-gated first-principles feedback framework that integrates multi-agent automated DFT, evidence stratification, generative structure proposal, acquisition ranking, and auditable release. Using 516 2DMatPedia-derived candidates, the workflow produced 280 QC-passed materials and 573 retained carrier-direction seed channels after channel-level reliability gating. These records were split into two feedback objects: relaxed structures updated the generative model, while retained mobility channels trained the acquisition model and set validation priority. Over multiple iterations, InvDesMobility screened 2.4 x 10^6 structures, submitted 102 candidates for DFT validation, and retained 86 reliability-gated generated channels across 41 formulas. Overall, the main contribution is not a fixed list of high-mobility materials, but a transferable feedback contract that makes closed-loop inverse design both useful and auditable when learning from expensive calculated properties. All source data, retained feedback records, and workflows are available at https://github.com/DreamLufei/invDesMobility, with an accompanying evidence website at https://dreamlufei.github.io/invDesMobility/.

cond-mat.mtrl-sci↗