arXiv subjects

Ze-Feng Gao

Publications and source records attributed to Ze-Feng Gao.

At least 37 records · Page 2Linked to original sources

arXiv2025

Anomalous charge density wave in altermagnetism

Exploring the intricate interplay between magnetism and charge density waves has long been a fundamental pursuit at the forefront of condensed matter research. In this letter, based on symmetry analysis and first-principles calculations, we propose for the first time that anomalous charge density wave can be realized in two-dimensional altermagnetic WO. The anomalous charge density wave is characterized by three key features: (i) Unlike conventional charge density wave, whose stabilization is driven by the opening of a gap near the Fermi level, the anomalous charge density wave is stabilized by the occupied states with energies shifting lower far away from the Fermi level; (ii) the anomalous charge density wave increases the density of states near the Fermi level and then enhances-rather than diminishes-the metallicity of materials; (iii) altermagnetism plays a crucial role in stabilizing anomalous charge density wave. Thus, our work offers a pathway for exploring both the realization and the underlying mechanisms of anomalous charge density waves in magnetic systems.

cond-mat.mtrl-sci↗

arXiv2025

Superconductivity in atom-intercalated quaternary hydrides under ambient pressure

Hydrogen-rich materials are the most promising candidates for high-temperature conventional superconductors under ambient pressure. Multinary hydrides have abundant structural configurations and are more promising to find high-temperature superconductors at ambient pressure, but searching for multinary materials in complex phase space is a great challenge. In this work, we used our developed AI search engine (InvDesFlow) to perform extensive investigations regarding ambient stable superconducting hydrides. Several quaternary hydrides with high superconducting temperature~($T_c$) are predicted. In particular, the superconducting $T_c$ of K$_2$GaCuH$_6$ and K$_2$LiCuH$_6$ are calculated to be 68~K and 53~K under ambient pressure, respectively, which shows a significant enhancement in comparison with that of K$_2$CuH$_6$~($T_c$ $\sim$ 16~K). We also find that intercalating atoms could cause phonon softening and induce more phonon modes with strong electron-phonon coupling. Hence, we propose that intercalating atoms is a feasible approach in searching for superconducting quaternary hydrides.

cond-mat.supr-con↗

arXiv2025

Unconventional compensated magnetic material LaMn$_2$SbO$_6$

Unconventional magnetism including altermagnetism and unconventional compensated magnetism, characterized by its duality of real-space antiferromagnetic alignment and momentum-space spin splitting, has garnered widespread attention. While altermagnetism has been extensively studied, research on unconventional compensated magnetism remains very rare. In particular, unconventional compensated magnetic materials are only theoretically predicted and have not yet been synthesized experimentally. In this study, based on symmetry analysis and the first-principles electronic structure calculations, we predict that LaMn$_2$SbO$_6$ is a unconventional compensated magnetic semiconductor. Given that the Mn ions at opposite spin lattice cannot be connected by any symmetry, the spin splitting in LaMn$_2$SbO$_6$ is isotropic. More importantly, LaMn$_2$SbO$_6$ has already been synthesized experimentally, and its magnetic structure has been confirmed by neutron scattering experiments. Therefore, LaMn$_2$SbO$_6$ serves as an excellent material platform for investigating the novel physical properties of unconventional compensated magnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

High-temperature superconductivity in Li$_2$AuH$_6$ mediated by strong electron-phonon coupling under ambient pressure

We used our developed AI search engine~(InvDesFlow) to perform extensive investigations regarding ambient stable superconducting hydrides. A cubic structure Li$_2$AuH$_6$ with Au-H octahedral motifs is identified to be a candidate. After performing thermodynamical analysis, we provide a feasible route to experimentally synthesize this material via the known LiAu and LiH compounds under ambient pressure. The further first-principles calculations suggest that Li$_2$AuH$_6$ shows a high superconducting transition temperature ($T_c$) $\sim$ 140 K under ambient pressure. The H-1$s$ electrons strongly couple with phonon modes of vibrations of Au-H octahedrons as well as vibrations of Li atoms, where the latter is not taken seriously in other previously similar cases. Hence, different from previous claims of searching metallic covalent bonds to find high-$T_c$ superconductors, we emphasize here the importance of those phonon modes with strong electron-phonon coupling (EPC). And we suggest that one can intercalate atoms into binary or ternary hydrides to introduce more potential phonon modes with strong EPC, which is an effective approach to find high-$T_c$ superconductors within multicomponent compounds.

cond-mat.supr-con↗

arXiv2025

InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors

The discovery of new superconducting materials, particularly those exhibiting high critical temperature ($T_c$), has been a vibrant area of study within the field of condensed matter physics. Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases. However, the known materials only scratch the surface of the extensive array of possibilities within the realm of materials. Here, we develop InvDesFlow, an AI search engine that integrates deep model pre-training and fine-tuning techniques, diffusion models, and physics-based approaches (e.g., first-principles electronic structure calculation) for the discovery of high-$T_c$ superconductors. Utilizing InvDesFlow, we have obtained 74 dynamically stable materials with critical temperatures predicted by the AI model to be $T_c \geq$ 15 K based on a very small set of samples. Notably, these materials are not contained in any existing dataset. Furthermore, we analyze trends in our dataset and individual materials including B$_4$CN$_3$ (at 5 GPa) and B$_5$CN$_2$ (at ambient pressure) whose $T_c$s are 24.08 K and 15.93 K, respectively. We demonstrate that AI technique can discover a set of new high-$T_c$ superconductors, outline its potential for accelerating discovery of the materials with targeted properties.

cond-mat.supr-con↗

arXiv2025

AI-accelerated Discovery of Altermagnetic Materials

Altermagnetism, a new magnetic phase, has been theoretically proposed and experimentally verified to be distinct from ferromagnetism and antiferromagnetism. Although altermagnets have been found to possess many exotic physical properties, the limited availability of known altermagnetic materials hinders the study of such properties. Hence, discovering more types of altermagnetic materials with different properties is crucial for a comprehensive understanding of altermagnetism and thus facilitating new applications in the next generation information technologies, e.g., storage devices and high-sensitivity sensors. Since each altermagnetic material has a unique crystal structure, we propose an automated discovery approach empowered by an AI search engine that employs a pre-trained graph neural network to learn the intrinsic features of the material crystal structure, followed by fine-tuning a classifier with limited positive samples to predict the altermagnetism probability of a given material candidate. Finally, we successfully discovered 50 new altermagnetic materials that cover metals, semiconductors, and insulators confirmed by the first-principles electronic structure calculations. The wide range of electronic structural characteristics reveals that various novel physical properties manifest in these newly discovered altermagnetic materials, e.g., anomalous Hall effect, anomalous Kerr effect, and topological property. Noteworthy, we discovered 4 $i$-wave altermagnetic materials for the first time. Overall, the AI search engine performs much better than human experts and suggests a set of new altermagnetic materials with unique properties, outlining its potential for accelerated discovery of the materials with targeted properties.

cond-mat.mtrl-sci↗

arXiv2025

Extremely strong spin-orbit coupling effect in light-element altermagnetic materials

Spin-orbit coupling is a key to realize many novel physical effects in condensed matter physics. Altermagnetic materials possess the duality of real-space antiferromagnetism and reciprocal-space ferromagnetism. It has not been explored that achieving strong spin-orbit coupling effect in light-element altermagnetic materials. In this work, based on symmetry analysis, the first-principles electronic structure calculations plus Dynamical Mean Field Theory, we demonstrate that there is strong spin-orbit coupling effect in light-element altermagnetic materials $\rm NiF_3$ and $\rm FeCO_3$, and then propose a mechanism for realizing such effective spin-orbit coupling. This mechanism reveals the cooperative effect of crystal symmetry, electron occupation, electronegativity, electron correlation, and intrinsic spin-orbit coupling. Our work provides an approach for searching light-element altermagnetic materials with an effective strong spin-orbit coupling.

cond-mat.mtrl-sci↗

arXiv2025

Crystal valley Hall effect

The time-reversal symmetry is thought to be a necessary condition for realizing valley Hall effect. If the time-reversal symmetry is broken, whether the valley Hall effect can be realized has not been explored. In this letter, based on symmetry analysis and the first-principles electronic structure calculations, we demonstrate that the vally Hall effect without time-reversal symmetry can be realized in two-dimensional altermagnetic materials Fe$_2$WSe$_4$ and Fe$_2$WS$_4$. Due to crystal symmetry required, the vally Hall effect without time-reversal symmetry is called crystal vally Hall effect. In addition, under uniaxial strain, both monolayer Fe$_2$WSe$_4$ and Fe$_2$WS$_4$ can realize piezomagnetic effect. Under biaxial compressive stress, both monolayer Fe$_2$WSe$_4$ and Fe$_2$WS$_4$ will transform from altermagnetic semiconductor phase to bipolarized topological Weyl semimetal phase. Our work not only provides a new direction for exploring the novel valley Hall effect, but also provides a good platform for exploring altermagnetic semiconductors and altermagnetic topological phase transitions.

cond-mat.mtrl-sci↗

arXiv2025

Luttinger compensated bipolarized magnetic semiconductor

Altermagnetic materials, with real-space antiferromagnetic arrangement and reciprocal-space anisotropic spin splitting, have attracted much attention. However, the spin splitting is small in most altermagnetic materials, which is a disadvantage to their application in electronic devices. In this study, based on symmetry analysis and the first-principles electronic structure calculations, we predict for the first time two Luttinger compensated bipolarized magnetic semiconductors Mn(CN)2 and Co(CN)2 with isotropic spin splitting as in the ferromagnetic materials. Our further analysis shows that the Luttinger compensated magnetism here depends not only on spin group symmetry, but also on the crystal field splitting and the number of d-orbital electrons. In addition, the polarized charge density indicates that both Mn(CN)2 and Co(CN)2 have the quasi-symmetry Tτ , resulting from the crystal field splitting and the number of d-orbital electrons. The Luttinger compensated magnetism not only has the zero total magnetic moment as the antiferromagnetism, but also has the isotropic spin splitting as the ferromagnetism, thus our work not only provides theoretical guidance for searching Luttinger compensated magnetic materials with distinctive properties, but also provides a material basis for the application in spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

AI-driven inverse design of materials: Past, present and future

The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through a large number of experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic structure calculation methods, such as the density functional theory and high-throughput computational methods. Recently, the rapid development of artificial intelligence technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures, thus opening up an efficient paradigm for the inverse design of functional materials. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers. Considering this rapid technological progress, in this survey, we look back on the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining issues for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers.

cond-mat.mtrl-sci↗

arXiv2024

Type-II Dirac nodal chain semimetal CrB4

Dirac nodal line semimetals with topologically protected drumhead surface states have attracted intense theoretical and experimental attention over a decade. However, the study of type-II Dirac nodal line semimetals is rare, especially the type-II nodal chain semimetals have not been confirmed by experiment due to the lack of ideal material platform. In this study, based on symmetry analysis and the first-principles electronic structure calculations, we predict that CrB4 is an ideal type-II Dirac nodal chain semimetal protected by the mirror symmetry. Moreover, there are two nodal rings protected by both space-inversion and time-reversal symmetries in CrB4. More importantly,in CrB4 the topologically protected drumhead surface states span the entire Brillouin zone at the Fermi level. On the other hand, the electron-hole compensation and the high mobility of Dirac fermions can result in large magnetoresistance effects in CrB4 according to the two-band model.Considering the fact that CrB4 has been synthesized experimentally and the spin-orbit coupling is very weak, CrB4 provides an ideal material platform for studying the exotic properties of type-II Dirac nodal chain semimetals in experiment.

cond-mat.mtrl-sci↗

arXiv2024

Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation

Increased training parameters have enabled large pre-trained models to excel in various downstream tasks. Nevertheless, the extensive computational requirements associated with these models hinder their widespread adoption within the community. We focus on Knowledge Distillation (KD), where a compact student model is trained to mimic a larger teacher model, facilitating the transfer of knowledge of large models. In contrast to much of the previous work, we scale up the parameters of the student model during training, to benefit from overparameterization without increasing the inference latency. In particular, we propose a tensor decomposition strategy that effectively over-parameterizes the relatively small student model through an efficient and nearly lossless decomposition of its parameter matrices into higher-dimensional tensors. To ensure efficiency, we further introduce a tensor constraint loss to align the high-dimensional tensors between the student and teacher models. Comprehensive experiments validate the significant performance enhancement by our approach in various KD tasks, covering computer vision and natural language processing areas. Our code is available at https://github.com/intell-sci-comput/OPDF.

cs.AI↗

arXiv2024

YuLan: An Open-source Large Language Model

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training details hinders further research and development. This paper presents the development of YuLan, a series of open-source LLMs with $12$ billion parameters. The base model of YuLan is pre-trained on approximately $1.7$T tokens derived from a diverse corpus, including massive English, Chinese, and multilingual texts. We design a three-stage pre-training method to enhance YuLan's overall capabilities. Subsequent phases of training incorporate instruction-tuning and human alignment, employing a substantial volume of high-quality synthesized data. To facilitate the learning of complex and long-tail knowledge, we devise a curriculum-learning framework throughout across these stages, which helps LLMs learn knowledge in an easy-to-hard manner. YuLan's training is finished on Jan, 2024 and has achieved performance on par with state-of-the-art LLMs across various English and Chinese benchmarks. This paper outlines a comprehensive technical roadmap for developing LLMs from scratch. Our model and codes are available at https://github.com/RUC-GSAI/YuLan-Chat.

cs.CL↗

arXiv2024

Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression

Key-value~(KV) caching is an important technique to accelerate the inference of large language models~(LLMs), but incurs significant memory overhead. To compress the size of KV cache, existing methods often compromise precision or require extra data for calibration, limiting their practicality in LLM deployment. In this paper, we introduce \textbf{DecoQuant}, a novel data-free low-bit quantization technique based on tensor decomposition methods, to effectively compress KV cache. Our core idea is to adjust the outlier distribution of the original matrix by performing tensor decomposition, so that the quantization difficulties are migrated from the matrix to decomposed local tensors. Specially, we find that outliers mainly concentrate on small local tensors, while large tensors tend to have a narrower value range. Based on this finding, we propose to apply low-bit quantization to the large tensor, while maintaining high-precision representation for the small tensor. Furthermore, we utilize the proposed quantization method to compress the KV cache of LLMs to accelerate the inference and develop an efficient dequantization kernel tailored specifically for DecoQuant. Through extensive experiments, DecoQuant demonstrates remarkable efficiency gains, showcasing up to a $\sim$75\% reduction in memory footprint while maintaining comparable generation quality.

cs.CL↗

arXiv2023

Altermagnetic ferroelectric LiFe2F6 and spin-triplet excitonic insulator phase

Altermagnetism is a new magnetic phase with k-dependent spin polarization and may exist in an insulating state with a high Néel temperature. This provides a new opportunity to obtain both spin and electric polarization in one material. Here, based on symmetry analysis and the first-principles electronic structures calculations, we predict that LiFe2F6 is a d-wave altermagnetic and charge-ordering-mediated ferroelectric material. Moreover, the LiFe2F6 transforms into a ferrimagnetic and ferroelectric phase with strong magnetoelectric coupling under biaxial compressive strain. Interestingly, the spins of the valence band and the conduction band are opposite in ferrimagnetic LiFe2F6, which facilitates a simultaneous spin-triplet excitonic insulator phase. More importantly, the spin-triplet excitons with spin 1 and -1 can be switched by electric fields in ferrimagnetic LiFe2F6 due to strong magnetoelectric coupling. Due to the abundance of novel physical properties, LiFe2F6 will certainly attract a wide range of theoretical and experimental interest.

cond-mat.mtrl-sci↗

arXiv2023

Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study

Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have been widely applied to reduce the memory footprint of LLMs as well as increasing the inference rate. However, a major challenge is that low-bit quantization methods often lead to performance degradation. It is important to understand how quantization impacts the capacity of LLMs. Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \emph{emergent abilities}, which are important characteristics that distinguish LLMs from small language models. Specially, we examine the abilities of in-context learning, chain-of-thought reasoning, and instruction-following in quantized LLMs. Our empirical experiments show that these emergent abilities still exist in 4-bit quantization models, while 2-bit models encounter severe performance degradation on the test of these abilities. To improve the performance of low-bit models, we conduct two special experiments: (1) fine-gained impact analysis that studies which components (or substructures) are more sensitive to quantization, and (2) performance compensation through model fine-tuning. Our work derives a series of important findings to understand the impact of quantization on emergent abilities, and sheds lights on the possibilities of extremely low-bit quantization for LLMs.

cs.CL↗

arXiv2023

Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture

In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models (PLMs) to a deeper model depth. Unlike prior work that shares all parameters or uses extra blocks, we design a more capable parameter-sharing architecture based on matrix product operator (MPO). MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts: the major part that contains the major information (central tensor) and the supplementary part that only has a small proportion of parameters (auxiliary tensors). Based on such a decomposition, our architecture shares the central tensor across all layers for reducing the model size and meanwhile keeps layer-specific auxiliary tensors (also using adapters) for enhancing the adaptation flexibility. To improve the model training, we further propose a stable initialization algorithm tailored for the MPO-based architecture. Extensive experiments have demonstrated the effectiveness of our proposed model in reducing the model size and achieving highly competitive performance.

cs.CL↗

arXiv2022

Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models

Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models. However, MoE requires incorporating significantly more parameters than the base model being extended. In this paper, we propose building a parameter-efficient MoE architecture by sharing information among experts. We adopt the matrix product operator (MPO, a tensor decomposition from quantum many-body physics) to reconstruct the parameter matrix in the expert layer and increase model capacity for pre-trained language models by sharing parameters of the central tensor (containing the core information) among different experts while enabling the specificity through the auxiliary tensors (complementing the central tensor) of different experts. To address the unbalanced optimization issue, we further design the gradient mask strategy for the MPO-based MoE architecture. Extensive experiments based on T5 and GPT-2 show improved performance and efficiency of the pre-trained language model (27.2x reduction in total parameters for the superior model performance, compared with the Switch Transformers). Our code is publicly available at https://github.com/RUCAIBox/MPOE.

cs.CL↗