arXiv subjects

Zhong-Yi Lu

Publications and source records attributed to Zhong-Yi Lu.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2026

PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation

In recent years, generative artificial intelligence has made significant advances in the design of crystalline materials, giving rise to approaches based on graph neural networks, diffusion models, and large language models. Existing evaluations commonly follow the stability-uniqueness-novelty (S.U.N.) framework, where stability is primarily assessed using thermodynamic criteria, which do not fully capture the dynamical stability essential for a material's practical existence. Dynamical stability is a key determinant of whether a material can be synthesized and persist, with phonon spectrum calculations serving as the standard for its evaluation. However, the high computational cost of such calculations has prevented large-scale assessment of dynamical stability in generated crystals. In this work, we introduce PhononBench, the first large-scale benchmark for dynamical stability in AI-generated crystals. Leveraging the recently developed MatterSim interatomic potential, which achieves density-functional-theory (DFT)-level accuracy in phonon predictions across more than 10,000 materials, PhononBench enables efficient phonon calculations and dynamical-stability analysis for 133,838 crystal structures generated by 7 leading crystal generation models. PhononBench reveals a widespread limitation of current generative models: unless otherwise specified, all reported dynamical-stability metrics are evaluated at a phonon-frequency threshold of -0.1 THz, with the average dynamical-stability rate across all generated structures being only 32.15%, and the top-performing model, MatterGen, reaching just 45.05%.In addition, we identify 32,995 crystal structures that are phonon-stable across the entire Brillouin zone under a strict threshold of -0.001 THz. In addition, a web-based service is accessible at http://phononbench.cn/, enabling minute-level ultra-fast phonon predictions.

cond-mat.mtrl-sci↗

arXiv2026

Selective stabilization of antiferromagnetic orders in FeTe films via local strain engineering

The parent compound FeTe hosts a complex magnetic landscape that is highly susceptible to lattice distortions. Although theoretical models have predicted a bicollinear to dimer antiferromagnetic (AFM) phase transition under tensile strain, its experimental realization and deterministic control has remained elusive owing to severe magnetic frustration. Here, combining high-resolution scanning tunneling microscopy (STM) and density functional theory (DFT) calculations, we demonstrate the selective stabilization of bicollinear and dimer AFM orders in few-layer FeTe films via local uniaxial strain engineering. By mapping the strain fields near dislocation areas in FeTe films and FeTe/FeSe heterostructures, we establish a direct correspondence between specific strain components and the resulting magnetic ground states. We find that uniaxial compression along the Fe-Fe next-nearest-neighbor direction stabilizes the bicollinear AFM order, with the stripe orientation aligning parallel to the compression axis. Crucially, we report the experimental realization of the long-range dimer AFM order, which emerges under anisotropic strain along the Fe-Fe nearest-neighbor direction. This phase manifests as a distinct $\sqrt{2} \times \sqrt{2}$ electronic reconstruction and shares a common Neel temperature with the bicollinear phase. Our findings reveal that anisotropic strain effectively lifts the magnetic degeneracy among competing states. This work provides a robust strategy for the manipulation of elusive magnetic orders and offers insights into the interplay between lattice, spin, and electronic degrees of freedom in iron-based superconductors.

cond-mat.str-el↗

arXiv2026

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability

Imaginary phonon modes remain a practical bottleneck in computational materials screening because otherwise plausible structures can be locally dynamically unstable under a chosen workflow. Here we present PhononBench-MP40, a spectrum-resolved benchmark dataset of Materials Project-derived crystals for workflow-defined phonon stability. The dataset starts from 47,969 MP40 workflow tasks and provides 46,899 completed records with paired stability labels and local phonopy YAML spectra, including 16,683 Stable records and 30,216 completed-phonon unstable records. A further 1,067 relaxation failures are reported separately rather than merged into the completed phonon denominator. The release centers on the local YAML spectrum: the stability label, the lowest sampled frequency and any threshold-dependent relabeling are derived from that spectrum. The dataset is openly available through Science Data Bank at https://doi.org/10.57760/sciencedb.38735. A companion GitHub repository provides the calculation code and lightweight access utilities. PhononBench-MP40 provides an auditable reference for workflow-defined stability classification, minimum-frequency analysis, threshold studies and failure-aware triage, while keeping the reference workflow, data schema and interpretation boundaries explicit.

cs.AI↗

arXiv2026

Probing universal imaginary-time relaxation critical dynamics with infinite projected entangled pair states

We investigate the imaginary-time relaxation critical dynamics of the two-dimensional transverse-field Ising model using infinite projected entangled pair states (iPEPS) with the full-update strategy. Simulating directly in the thermodynamic limit, we explore the relaxation process near the critical point with two types of initial states: a fully polarized state and a product state with a small magnetization. For the fully polarized state, the magnetization shows a power law scaling $M\propto τ^{-β/(νz)}$ in the imaginary-time evolution, from which both the critical point and critical exponent can be determined with high accuracy. For the nearly paramagnetic state, the relaxation process exhibits a behavior of $M\propto τ^θ$ with $θ=0.1958$ being the critical initial-slip exponent, which is in good agreement with that obtained from the dynamic scaling of the self-correlation in quantum Monte Carlo method. These universal features emerge well before the system converges to the ground state, demonstrating the efficiency of imaginary-time evolution for probing quantum criticality. Our results demonstrate that iPEPS can serve as a robust and scalable method for studying dynamical critical phenomena in two-dimensional quantum many-body systems.

cond-mat.str-el↗

arXiv2026

Reversible tuning of magnetic order and intrinsic superconductivity in strained FeTe films via stoichiometry control

FeTe is a prototypical parent compound of iron-based superconductors. While bulk FeTe is non-superconducting with a long-range bicollinear antiferromagnetic order, superconductivity has been achieved in thin films. However, the approaches usually involve complex oxygen incorporation or interfacial effects, the microscopic mechanisms of which remain elusive. Here, we prepare high-purity, bare FeTe thin films on SrTiO3 and investigate their magnetic and superconducting states combining both microscopic and macroscopic characterizations. By reducing the interstitial Fe impurities, we successfully suppress the long-range antiferromagnetic order, enhance the quasiparticle coherence and induce superconductivity at ~10 K. Moreover, this process is readily reversible by tuning the Fe concentration. Our findings reveal that precise stoichiometric control is sufficient to induce intrinsic superconductivity in strained FeTe thin films. This work provides insights into the competition between magnetism and superconductivity in iron chalcogenides, and supplies methods for developing stable, high-purity superconducting FeTe films.

cond-mat.supr-con↗

arXiv2026

LEAP: A closed-loop framework for perovskite precursor additive discovery

Efficient discovery of precursor additives is essential for improving the performance of perovskite solar cells, yet the large chemical space makes conventional trial-and-error screening inefficient. We develop LEAP(LLM-driven Exploration via Active Learning for Perovskites), an expert-in-the-loop closed framework that couples a domain-specialized large language model(LLM) with active learning for iterative additive prioritization. The LLM is trained to extract mechanism-relevant knowledge from the perovskite additive literature and to represent candidate molecules through interpretable descriptors, which are further integrated into a Bayesian optimization workflow for uncertainty-aware prioritization under low-data conditions. Benchmark results on unseen literature show that the domain-specialized model outperforms general-purpose models in mechanism-consistent reasoning. Experimental validation in an expert-in-the-loop proof-of-concept study suggests improved additive prioritization across three screening rounds, leading to average device PCEs of 20.13% and 20.87% for the later-round 6-CDQ- and 2-CNA-treated devices, respectively, compared with 19.25% for the control, with a champion PCE of 21.32%. These results provide preliminary evidence that literature-grounded mechanistic descriptors, when coupled with Bayesian optimization and expert feasibility review, can support mechanism-aware additive prioritization in perovskite photovoltaics.

cs.LG↗

arXiv2026

InvDesFlow-AL: active learning-based workflow for inverse design of functional materials

Developing inverse design methods for functional materials with specific properties is critical to advancing fields like renewable energy, catalysis, energy storage, and carbon capture. Generative models based on diffusion principles can directly produce new materials that meet performance constraints, thereby significantly accelerating the material design process. However, existing methods for generating and predicting crystal structures often remain limited by low success rates. In this work, we propose a novel inverse material design generative framework called InvDesFlow-AL, which is based on active learning strategies. This framework can iteratively optimize the material generation process to gradually guide it towards desired performance characteristics. In terms of crystal structure prediction, the InvDesFlow-AL model achieves an RMSE of 0.0423 Å, representing an 32.96% improvement in performance compared to exsisting generative models. Additionally, InvDesFlow-AL has been successfully validated in the design of low-formation-energy and low-Ehull materials. It can systematically generate materials with progressively lower formation energies while continuously expanding the exploration across diverse chemical spaces. These results fully demonstrate the effectiveness of the proposed active learning-driven generative model in accelerating material discovery and inverse design. To further prove the effectiveness of this method, we took the search for BCS superconductors under ambient pressure as an example explored by InvDesFlow-AL. As a result, we successfully identified Li$_2$AuH$_6$ as a conventional BCS superconductor with an ultra-high transition temperature of 140 K. This discovery provides strong empirical support for the application of inverse design in materials science.

cond-mat.mtrl-sci↗

arXiv2026

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design

Perovskite solar cells (PSCs) have rapidly emerged as a leading contender in next-generation photovoltaic technologies, owing to their exceptional power conversion efficiencies and advantageous material properties. Despite these advances, challenges such as long-term stability, environmental sustainability, and scalable manufacturing continue to hinder their commercialization. Precursor additive engineering has shown promise in addressing these issues by enhancing both the performance and durability of PSCs. However, the explosive growth of scientific literature and the complex interplay of materials, processes, and device architectures make it increasingly difficult for researchers to efficiently access, organize, and utilize domain knowledge in this rapidly evolving field. To address this gap, we introduce Perovskite-R1, a specialized large language model (LLM) with advanced reasoning capabilities tailored for the discovery and design of PSC precursor additives. By systematically mining and curating 1,232 high-quality scientific publications and integrating a comprehensive library of 33,269 candidate materials, we constructed a domain-specific instruction-tuning dataset using automated question-answer generation and chain-of-thought reasoning. Fine-tuning the QwQ-32B model on this dataset resulted in Perovskite-R1, which can intelligently synthesize literature insights and generate innovative and practical solutions for defect passivation and the selection of precursor additives. Experimental validation of several model-proposed strategies confirms their effectiveness in improving material stability and performance. Our work demonstrates the potential of domain-adapted LLMs in accelerating materials discovery and provides a closed-loop framework for intelligent, data-driven advancements in perovskite photovoltaic research.

cs.LG↗

arXiv2026

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction

The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of these methods. In this work, we present the HTSC-2025, an ambient-pressure high-temperature superconducting benchmark dataset. This comprehensive compilation encompasses theoretically predicted superconducting materials discovered by theoretical physicists from 2023 to 2025 based on BCS superconductivity theory, including the renowned X$_2$YH$_6$ system, perovskite MXH$_3$ system, M$_3$XH$_8$ system, cage-like BCN-doped metal atomic systems derived from LaH$_{10}$ structural evolution, and two-dimensional honeycomb-structured systems evolving from MgB$_2$. The HTSC-2025 benchmark has been open-sourced at https://github.com/xqh19970407/HTSC-2025 and will be continuously updated. This benchmark holds significant importance for accelerating the discovery of superconducting materials using AI-based methods.

cond-mat.supr-con↗

arXiv2026

Strategic Over-Parameterization for Generalizable Low-Rank Adaptation

Adapting large language models (LLMs) to downstream tasks via full fine-tuning is increasingly impractical due to its computational and memory demands. Parameter-efficient fine-tuning (PEFT) approaches such as Low-Rank Adaptation (LoRA) mitigate this by confining updates to a compact set of trainable parameters, but this aggressive reduction often sacrifices generalization, especially under transfer across heterogeneous tasks and domains. We revisit the tension between parameter efficiency and adaptation capacity, and ask whether the two are truly at odds. We answer in the negative by introducing LoRA-Over, a framework grounded in a simple principle: enrich the optimization landscape during training, then collapse the enrichment at inference. LoRA-Over injects auxiliary parameters into the low-rank adapters during training to broaden the effective hypothesis space, and through a decomposition-based reformulation folds them back into a standard low-rank structure with negligible reconstruction error, keeping inference cost identical to vanilla LoRA. Since not all weight matrices benefit equally from added capacity, we further propose two scheduling strategies, one statically predefined and one dynamically determined at runtime, that direct extra capacity where most needed. We evaluate LoRA-Over on language understanding (GLUE, T5-Base), dialogue (MT-Bench), arithmetic reasoning (GSM8K), and code generation (HumanEval), using LLaMA 2-7B and LLaMA 3.1-8B. Across all benchmarks and scales, LoRA-Over consistently outperforms vanilla LoRA, showing that principled over-parameterization designed to vanish at inference is an effective lever for improving PEFT generalization. Code will be released upon acceptance.

cs.LG↗

arXiv2026

A DFT+DMFT study of the electronic structure of Samarium

The electronic structure of Samarium (Sm) was calculated using the density functional theory combined with the single-site dynamical mean-field theory. In this work, we investigated the electronic properties of α, \b{eta} and γ phases at ambient pressure, including the band structures, density of states, self-energy functions and valence state histograms. Our results agree with the experimental data.The calculation shows that the 4f electrons in all these phases are well localized, the Kondo peaks are suppressed and the hybridization between the 4f electrons and conduction electrons are quite weak. Our results also show the strong correlation effect is significant in Sm metal.

cond-mat.str-el↗

arXiv2026

Electronic structure and correlation of La$_4$Co$_2$NiO$_8$Cl$_2$: a theoretical proposal for a La$_4$Ni$_3$O$_{10}$-like high-temperature superconductor

Based on the discovery of high-temperature superconductivity in the bilayer nickelate La$_3$Ni$_2$O$_7$, several Co-based La$_3$Ni$_2$O$_7$-like materials were theoretically predicted as possible high-temperature superconductors by electron doping. Motivated by these findings and the subsequent discovery of superconductivity in the trilayer nickelate La$_4$Ni$_3$O$_{10}$ under high pressure, we propose and investigate a Co-based La$_4$Ni$_3$O$_{10}$-like material. With electron doping to the high-pressure trilayer cobaltate La$_4$Co$_3$O$_{10}$, using density functional theory combined with dynamical mean-field theory (DFT+DMFT), we find that the resulting compound La$_4$Co$_2$NiO$_8$Cl$_2$ exhibits a crystal structure and a strongly correlated electronic structure similar to those of La$_4$Ni$_3$O$_{10}$ under high pressure. This suggests that this new compound may host high-temperature superconductivity.

cond-mat.str-el↗

arXiv2026

Excitonic Quantum Anomalous Hall Effect in Collinear Magnets Without Spin-Orbit Coupling

Spin-orbit coupling (SOC) is thought to be necessary in realizing quantum anomalous Hall (QAH) insulators in magnetic materials. In this Letter, we propose an exciton-condensation mechanism to realize QAH effect in collinear magnets with negligible spin-orbit coupling. This mechanism is realized by two steps: first prepare a spin-splitting nodal-ring band structure, and then gap out the nodal-ring via triplet exciton condensation. A nonzero Chern number can be obtained if the in-plane spin texture resulting from the triplet exciton condensation is noncollinear in momentum space. We show that the electron-phonon coupling can switch the spin texture from a colinear pattern to a noncolinear one and plays an essential role in realizing QAH effect. The above mechanism is not only suitable for ferrogmagnets but also applicable for altermagnets. Finally, through first-principles calculations we propose the bilayer material V2SeTeO to be a promising candidate of excitonic QAH insulator.

cond-mat.mes-hall↗

arXiv2026

$σ$ bands driven high-temperature superconductivity in hydrogenated hexagonal BC$_3$ monolayer

Material with metallic $σ$-bonding bands is expected to be a high-temperature superconductor, due to the sensitivity of $σ$ electrons to lattice vibration. Based on the first-principles calculations, electronic structures of hydrogenated BC$_3$ monolayers (H$_n$-B$_2$C$_6$ with $n$=1-8) are systematically investigated. At high coverage of hydrogen, the monolayer stabilizes in chair-like $sp^3$-hybridized configurations, leading to the metallization of $σ$ bands, especially in H$_7$-B$_2$C$_6$ and H$_8$-B$_2$C$_6$. This metallicity originates from the electron deficiency of boron, compared with insulating graphane. Utilizing Wannier interpolation, the electron-phonon coupling strengths for metallic phases of H$_n$-B$_2$C$_6$ are determined. As expected, strong couplings are identified between the conducting $σ$ electrons and low-frequency phonon modes. By solving the anisotropic Eliashberg equations, we confirm that H$_7$-B$_2$C$_6$ and H$_8$-B$_2$C$_6$ are single-gap superconductors with critical temperature being 87 K, exceeding the boiling point of liquid nitrogen. Considering that monolayer BC$_3$ has been synthesized in experiment, our results demonstrate that hydrogenation of two-dimensional BC$_3$ provides a viable pathway to achieve high-temperature superconductivity at ambient pressure.

cond-mat.supr-con↗

arXiv2026

Single Pair of Charge-two Weyl Fermions in Chiral Boron Allotropes

The realization of a minimal Weyl semimetal (WSM) hosting a single pair of Weyl points (WPs) has thus far been restricted to magnetic systems, since time-reversal symmetry generally enforces a minimum of four WPs in nonmagnetic materials. Here, combining first-principles calculations with symmetry analysis, we identify two stable boron allotropes, chiral HDSBC-B$_{20}$ and cage-like CR-B$_{12}$, as the first nonmagnetic electronic materials realizing a single pair of WPs in the spinless regime. We show that the interplay between time-reversal symmetry and crystallographic rotation symmetry ($C_4$ or $C_3$) stabilizes exactly one pair of $C=2$ WPs pinned at time-reversal-invariant momenta, thereby circumventing the conventional node-quartet constraint. These double-WPs exhibit linear dispersion along the rotation axis and quadratic dispersion in the perpendicular plane. In HDSBC-B$_{20}$, the sign of the topological charge is directly correlated with structural chirality. Both materials host exceptionally long double Fermi arcs spanning the surface Brillouin zone, providing experimentally accessible signatures. Our findings establish nonmagnetic material platforms for minimal double-Weyl fermions and broaden the landscape of unconventional WSMs.

cond-mat.mtrl-sci↗

arXiv2026

Prediction of several Co-based La$_3$Ni$_2$O$_7$-like superconducting materials

High-temperature superconductivity has been found in Fe-, Ni-, and Cu-based compounds but has remained elusive in Co-based materials. The recent discovery of superconductivity in pressurized bilayer nickelate La$_3$Ni$_2$O$_7$ has renewed interest in related layered systems. Here, we predict several Co-based analogs that may realize similar physics. Electron doping of the high-pressure bilayer cobaltate La$_3$Co$_2$O$_7$ yields LaTh$_2$Co$_2$O$_7$, La$_3$Ni$_2$O$_5$Cl$_2$, and La$_3$Ni$_2$O$_5$Br$_2$, which exhibit closely related crystal structures and strongly correlated electronic states. Random-phase-approximation calculations reveal $s$-wave as the leading pairing symmetry in these compounds.

cond-mat.supr-con↗

arXiv2026

Strain-triggered high-temperature superconducting transition in two-dimensional carbon allotrope

Driving non-superconducting materials into a superconducting state through specific modulation is a key focus in the field of superconductivity. Pressure is a powerful method that can switch a three-dimensional (3D) material between non-superconducting and superconducting states. In the two-dimensional (2D) case, strain engineering plays a similar role to pressure. However, purely strain-induced superconductivity in 2D systems remains exceedingly scarce. Using first-principles calculations, we demonstrate that a superconducting transition can be induced solely by applying biaxial tensile strain in a 2D carbon allotrope, THO-graphene, which is composed of triangles, hexagons, and octagons. Free-standing THO-graphene is non-superconducting. Surprisingly, the electron-phonon coupling in strained THO-graphene is enhanced strong enough to pair electrons and realize superconductivity, with the highest superconducting transition temperature reaching 45 K. This work not only provides a notable example of controlling metal-superconductor transition in 2D system just via strain, but also sets a new record of superconducting transition temperature for 2D elemental superconductors.

cond-mat.supr-con↗