arXiv subjects

Cai-Zhuang Wang

Publications and source records attributed to Cai-Zhuang Wang.

At least 19 recordsLinked to original sources

arXiv2026

First-Principles Study of Magnetism, Electronic Structure, and Bonding in Nb-Mn-Ge Kagome Compounds

In this work, we systematically investigate the magnetic ground states, electronic structures, and bonding characteristics of the computationally predicted stable NbMn6Ge6, NbMn6Ge5, and NbMn6Ge4 using first-principles calculations. Our results show that structurally stable NbMn6Ge6 has a collinear antiferromagnetic configuration, while the metastable rhombohedral NbMn6Ge5 and NbMn6Ge4 favor ferromagnetic ground states. Magnetic moments on Mn atoms are nearly localized, suggesting the applicability of a generalized spin Hamiltonian. Magnetic anisotropy in AFM NbMn6Ge6 and FM NbMn6Ge5 has uniaxial behavior, while FM NbMn6Ge4 has in-plane anisotropy. Charge density difference and electron localization function analyses further show charge redistribution and bonding features within the Mn-Ge Kagome network and between adjacent structural layers. Electronic structures near the Fermi level show no features suitable for topological magnetism studies. Experimental synthesis, structural characterization, and magnetic measurements are required to verify our predictions.

cond-mat.mtrl-sci↗

arXiv2026

Emergent Noncollinearity and Near-Degenerate Magnetic Superlattices in AT6X6 Kagome Metals

Ferromagnetic AT6X6 Kagome compounds are a popular class of systems in which quantum magnetism with topological features has been observed. These systems allow easy chemical substitution, creating an opportunity to fine-tune their properties. In this paper, we present electronic-structure and magnetic ground-state studies of several AT6X6 compounds with relatively low magnetic-ground-state stability. We find unusual magnetic orderings, including complex spin-spiral states and the formation of magnetic long-range superstructures. While LiFe6Ga6 and TiMn6Ge6 retain collinear AFM ground states with low-energy FM/AFM layer sequences, competing spin-spiral and long-period antiferromagnetic structures in MgFe6Ga6 and a double-spin-spiral ground state in TiFe6Ga6 were determined. Magnetism in all these systems appears local, with adiabatic energy profiles suggesting non-Heisenberg long-range interactions, including a strong biquadratic term. In TiMn6Ge6, we found the conditions for magnetic tunneling. Our results show that, in addition to traditional magnetic topological features in such FM Kagome systems, near-degenerate magnetic superstructures suitable for spintronic switching applications can form naturally. Overall, these systems represent a potentially rich playground for neutron diffraction and spintronics experimental studies.

cond-mat.mtrl-sci↗

arXiv2026

Discovery of novel magnetic Y-Mn-B compounds via advanced machine learning guided framework

Rare-earth transition-metal borides offer critical structural motifs for permanent-magnet design; however, the manganese-rich regions within these compositional phase spaces remain largely unexplored. In this work, we develop an advanced machine-learning-assisted discovery framework to explore Y-Mn-B ternary system. Starting from over one million hypothetical structures generated from known structures in databases, we filtered promising candidates by first applying graph neural networks to predict material stability, then using machine-learning-interatomic-potential to relax their structures, and finally validating the results with first-principles calculations. We identify 5 stable and near-stable Y-Mn-B phases along with 61 metastable compounds with the formation energy within 100 meV/atom with respect to the ternary convex hull. Among them, Y2Mn7B7 and YMn4B4 are structurally analogous to the previously synthesized $R_{1+ε}Fe_4B_4$ 1D incommensurate composite chain compounds. In striking contrast to the strongly suppressed Fe moments reported, our first-principles calculations reveal that the predicted Mn-chain phases preserve sizable local Mn moments (approximately 1.1 $μ_B$) and favored ferromagnetic ordering. Electronic structure analyses elucidate the microscopic origin of moment recovery via an enhanced exchange splitting driven by a Stoner-like instability. We also perform systematic Mn-Fe substitution to confirm the thermodynamic continuity and a monotonic enhancement of the macroscopic magnetization, from Fe to Mn. These findings indicate that targeted transition-metal substitution within a one-dimensional boride family can recover transition-metal magnetism, offering a physically interpretable route for designing new magnetic rare-earth transition-metal borides.

cond-mat.mtrl-sci↗

arXiv2026

Antiferromagnetic Phases in Zr-Fe-Ge Kagome Systems

A wide variety of chemical substitutions in ferromagnetic Kagome systems can lead to diverse magnetic phases with electronic structures suitable for topological or quantum material properties. Here, we study the electronic structure and magnetic orderings using first-principles calculations for the magnetic Kagome compounds ZrFe6Ge6, ZrFe6Ge4, and ZrFe6Ge5. For ZrFe6Ge6, the obtained ground-state magnetic structure is A-type antiferromagnetic (AFM), in agreement with existing experiments. We predicted that the magnetic ground states of ZrFe6Ge4 and ZrFe6Ge5 are collinear A-type bilayer AFM structures with long-period ordering that involves a mix of FM and AFM interlayer orientations. The formation of such long-range magnetic structures appears to be a general feature and is not tied to specific substitutions. The magnetic moments in these systems are largely local and only weakly dependent on the magnetic configuration, with magnitudes in good agreement with available experimental estimates. Neutron scattering experiments, which could provide direct verification of these predictions, are therefore of particular importance.

cond-mat.mtrl-sci↗

arXiv2026

exa-PD: A scalable high-performance workflow for multi-element phase diagram construction

Exa-PD is a highly parallelizable workflow designed for the construction of multi-element phase diagrams (PDs). It uses standard sampling techniques, molecular dynamics (MD) and Monte Carlo (MC) as implemented in the LAMMPS package, to simultaneously sample multiple phases over a fine temperature-composition mesh for free-energy calculations. Parsl serves as the global workflow engine, coordinating large ensembles of MD and MC tasks to achieve massive parallelization with strong scalability. The resulting free energies of liquid and solid phases are then fed to CALPHAD modeling via the PyCalphad package to construct multi-element PDs.

cond-mat.mtrl-sci↗

arXiv2026

Complex crystal structure prediction using ML-enhanced multi-minima iterative genetic algorithm

Current machine learning (ML) approaches for materials discovery rely heavily on known structural databases, limiting their ability to identify entirely novel structure types. In this work, we develop a multi-minima iterative genetic algorithm (MMIGA) that integrates an artificial-neural-network machine learning (ANN-ML) interatomic potential with an iterative, metadynamics-inspired penalty scheme. We demonstrate the robustness of this method on a complex ternary La-Co-Pb system, characterized by Co-Pb immiscibility and an intricate energy landscape. The ML-enhanced MMIGA successfully predicts the ground-state Pbam structure of the recently synthesized La4Co4Pb antagonistic-pair-phase, a novel structure missed by previous database-reliant ML predictions, while also identifying multiple metastable competing phases. Additionally, we challenged the MMIGA method to predict the structure of La5CoPb2 antagonistic-pair-phase, a new compound discovered during earlier attempts to synthesize the predicted phase La3CoPb. With only knowledge of the composition, our MMIGA approach successfully predicts the orthorhombic structure of La5CoPb2, producing an exact match with the structure independently determined by x-ray diffraction. By efficiently mapping both global minimum and relevant competing metastable states, this approach provides critical theoretical insights into phase selection for novel quantum and magnetic materials.

cond-mat.mtrl-sci↗

arXiv2026

Fragile electron-phonon superconductivity in MnB4 under pressure

The origin of pressure-induced superconductivity in MnB4 remains unclear. Here we show that it can be explained by electron-phonon coupling once the structural space is mapped using both volume and the Mn dimer distance as key structural parameters under compression. Minor changes in the dimer distance significantly affect electronic and phonon properties, bringing the calculated Tc into agreement with experiment. Our results suggest that MnB4 is a highly responsive system, providing a platform for probing the subtle interplay between structural instability, superconductivity and magnetism.

cond-mat.supr-con↗

arXiv2026

Disentangling electronic and phononic contributions to high-temperature superconductivity in X2MH6 hydrides

Understanding the factors that control superconductivity is essential for discovering new superconducting materials using high-throughput elemental substitution. Focusing on the recently predicted ambient-pressure superconducting X2MH6 family, we disentangle the phononic and electronic contributions to Tc to determine how isoelectronic substitution alters superconductivity. While substitution affects both phononic and electronic properties, the electronic contribution plays the dominant role in determining Tc in the X2MH6 family. We show that the electronic contribution is affected by three key factors: the X-H bond distance, the electron localization function networking value of hydrogen, and the hydrogen-projected density of states at the Fermi level. A combined figure of merit derived from these parameters exhibits a robust correlation with Tc across the family. We further show that pressure produces competing effects on superconductivity: it enhances the electronic contribution by shortening X-H bonds, but simultaneously weaken the phononic contribution by increasing phonon frequencies. The net pressure dependence of Tc therefore results from the balance between these opposing tendencies. By disentangling and analyzing the electronic and phononic mechanisms, this work provides comprehensive insight into superconductivity in X2MH6 hydrides and offers practical guidance for designing new high-Tc hydride superconductors.

cond-mat.supr-con↗

arXiv2026

exaPD: A highly parallelizable workflow for multi-element phase diagram (PD) construction

Phase diagrams (PDs) illustrate the relative stability of competing phases under varying conditions, serving as critical tools for synthesizing complex materials. Reliable phase diagrams rely on precise free energy calculations, which are computationally intensive. We introduce exaPD, a user-friendly workflow that enables simultaneous sampling of multiple phases across a fine mesh of temperature and composition for free energy calculations. The package employs standard molecular dynamics (MD) and Monte Carlo (MC) sampling techniques, as implemented in the LAMMPS package. Various interatomic potentials are supported, including the neural network potentials with near {\it ab initio} accuracy. A global controller, built with Parsl, manages the MD/MC jobs to achieve massive parallelization with near ideal scalability. The resulting free energies of both liquid and solid phases, including solid solutions, are integrated into CALPHAD modeling using the PYCALPHAD package for constructing the phase diagram.

cond-mat.mtrl-sci↗

arXiv2025

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

We present exa-AMD, an open-source, high-performance framework designed for accelerated materials discovery on modern supercomputers. exa-AMD overcomes key computational bottlenecks in large-scale structure prediction through task-based parallelization, adaptive load balancing, and optimized data management for CPU and GPU architectures. The framework automates the end-to-end workflow, from generating candidate structures to evaluating formation energies and updating phase diagrams. Its modular design allows users to easily replace or extend components with custom machine learning models, alternative initial structure templates, and future structure generators, enabling flexible integration with emerging AI approaches. We demonstrate strong scaling across high-performance computing platforms and highlight applications to Na-B-C, Ce-Co-B, and Fe-Co-Zr systems, establishing exa-AMD as a robust and exascale-ready tool for accelerating the discovery and design of functional materials. exa-AMD is publicly available on GitHub, with detailed documentation and reproducible test cases to support community engagement and collaborative research.

cond-mat.mtrl-sci↗

arXiv2025

Pressure-Induced B1 to B2 Phase Transition in CeN Studied by ab initio Correlation Matrix Renormalization Theory Calculations

We apply correlation matrix renormalization theory (CMRT) to cerium nitride (CeN) under pressure. For B1 (NaCl-type) phase, CMRT gives an equation of state consistent with ambient pressure experiments. It produces electronic density-of-state (DOS) characterized by a sharp 4f quasi-particle resonance peak pinned at the Fermi level and two subbands formed by strong hybridization between the localized Ce-4f electrons and the itinerant Ce-5d and N-2p electrons below the Fermi level, consistent with XPS experiments. Upon compression, CMRT predicts a first-order B1 to B2 (CsCl-type) transition with ~11% volume collapse in agreement with experiments. Across the transition, the 4f spectral weight broadens, the 4f orbital occupancy increases, and the hybridization with conduction states enhances, signaling a crossover from partially localized to more itinerant 4f behavior. These features are in excellent agreement with experimental observations, demonstrating that CMRT provides a parameter-free description and prediction of correlation-driven structural and electronic transitions in rare-earth compounds.

cond-mat.str-el↗

arXiv2025

Ab initio calculation of atomic solid hydrogen phases based on Gutzwiller many-body wave functions

We apply two ab initio many-body methods based on Gutzwiller wave functions, i.e., correlation matrix renormalization theory (CMRT) and Gutzwiller conjugate gradient minimization (GCGM), to the study of crystalline phases of atomic hydrogen. Both methods avoid empirical Hubbard U parameters and are free from double-counting issues. CMRT employs a Gutzwiller-type approximation that enables efficient calculations, while GCGM goes beyond this approximation to achieve higher accuracy at higher computational cost. By benchmarking against available quantum Monte Carlo results, we demonstrate that while both methods are more accurate than the widely used density-functional theory (DFT), GCGM systematically captures additional correlation energy missing in CMRT, leading to significantly improved total energy predictions. We also show that by including the correlation energy $E_c$ from LDA in the CMRT calculation, CMRT+$E_c$ produces energy in better agreement with the QMC results in these hydrogen lattice systems.

cond-mat.str-el↗

arXiv2025

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

Rare earth (RE)-free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable interest. We performed a comprehensive search for new hard magnetic materials in the ternary Fe-Co-Zr space by leveraging a scalable, machine learning-assisted materials discovery framework running on GPU-enabled exascale computing resources. This framework integrates crystal graph convolutional neural network (CGCNN) machine learning (ML) method with first-principles calculations to efficiently navigate the vast composition-structure space. The efficiency and accuracy of the ML approach enable us to reveal 9 new thermodynamically stable ternary Fe-Co-Zr compounds and 81 promising low-energy metastable phases with their formation energies within 0.1 eV/atom above the convex hull. The predicted compounds span a wide range of crystal symmetries and magnetic behaviors, providing a rich platform for tuning functional properties. Based on the analysis of site-specific magnetic properties, we show that the Fe6Co17Zr6 compound obtained from our ML discovery can be further optimized by chemical doping. Chemical substitutions lead to a ternary Fe5Co18Zr6 phase with a strong anisotropy of K1 = 1.1 MJ/m3, and a stable quaternary magnetic Fe5Co16Zr6Mn4 compound.

cond-mat.mtrl-sci↗

arXiv2025

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design

exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows. The execution model of exa-AMD relies on Parsl, a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers. By using Parsl, exa-AMD is able to decouple the workflow logic from execution configuration, thereby empowering researchers to scale their workflows without having to reimplement them for each system.

cs.DC↗

arXiv2025

Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems

While molecular dynamics (MD) is a very useful computational method for atomistic simulations, modeling the interatomic interactions for reliable MD simulations of real materials has been a long-standing challenge. In 2007, Behler and Perrinello first proposed and demonstrated an artificial neural network machine learning (ANN-ML) scheme, opening a new paradigm for developing accurate and efficient interatomic potentials for reliable MD simulation studies of the thermodynamics and kinetics of materials. In this paper, we show that an accurate and transferable ANN-ML interatomic potential can be developed for MD simulations of La-Si-P system. The crucial role of training data in the ML potential development is discussed. The developed ANN-ML potential accurately describes not only the energy versus volume curves for all the known elemental, binary, and ternary crystalline structures in La-Si-P system, but also the structures of La-Si-P liquids with various compositions. Using the developed ANN-ML potential, the melting temperatures of several crystalline phases in La-Si-P system are predicted by the coexistence of solid-liquid phases from MD simulations. While the ANN-ML model systematically underestimates the melting temperatures of these phases, the overall trend agrees with experiment. The developed ANN-ML potential is also applied to study the nucleation and growth of LaP as a function of different relative concentrations of Si and P in the La-Si-P liquid, and the obtained results are consistent with experimental observations.

cond-mat.mtrl-sci↗

arXiv2025

Quantum-Classical Embedding via Ghost Gutzwiller Approximation for Enhanced Simulations of Correlated Electron Systems

Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. This work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

quant-ph↗

arXiv2025

Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements

The adaptive variational quantum dynamics simulation (AVQDS) method performs real-time evolution of quantum states using automatically generated parameterized quantum circuits that often contain substantially fewer gates than Trotter circuits. Here we report an improved version of the method, which we call AVQDS(T), by porting the Tiling Efficient Trial Circuits with Rotations Implemented Simultaneously (TETRIS) technique. The algorithm adaptively adds layers of disjoint unitary gates to the ansatz circuit so as to keep the McLachlan distance, a measure of the accuracy of the variational dynamics, below a fixed threshold. We perform benchmark noiseless AVQDS(T) simulations of quench dynamics in local spin models demonstrating that the TETRIS technique significantly reduces the circuit depth and two-qubit gate count. We also show a method based on eigenvalue truncation to solve the linear equations of motion for the variational parameters with enhanced noise resilience. Finally, we propose a way to substantially alleviate the measurement overhead of AVQDS(T) while maintaining high accuracy by synergistically integrating quantum circuit calculations on quantum processing units with classical calculations using, e.g., tensor networks to evaluate the quantum geometric tensor. We showcase that this approach enables AVQDS(T) to deliver more accurate results than simulations using a fixed ansatz of comparable final depth for a significant time duration with fewer quantum resources.

quant-ph↗

arXiv2025

Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs

The discovery of novel quantum materials within ternary phase spaces containing antagonistic pair such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds great potential. In this work, we investigate the stabilization of these immiscible pairs through the integration of Cerium (Ce), an abundant rare-earth and cost-effective element. By employing a machine learning (ML)-guided framework, particularly crystal graph convolutional neural networks (CGCNN), combined with first-principles calculations, we efficiently explore the composition/structure space and predict 9 stable and 37 metastable Ce-Fe-X (X=Bi, Pb, In and Ag) ternary compounds. Our findings include the identification of multiple new stable and metastable phases, which are evaluated for their structural and energetic properties. These discoveries not only contribute to the advancement of quantum materials but also offer viable alternatives to critical rare earth elements, underscoring the importance of Ce-based intermetallic compounds in technological applications.

cond-mat.mtrl-sci↗