arXiv subjects

Weiyi Xia

Publications and source records attributed to Weiyi Xia.

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

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

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

Machine learning driven search of hydrogen storage materials

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of ternary alloys (easily extendable to multi-principal-element alloys), such as Ti-Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co-Ni-X (X = Al, Mg, V). Ti-Nb-Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40-50%. We attributed to slow hydrogen kinetics in molybdenum rich alloys, which is validated by our pressure-composition isotherm (PCT) experiments on pure Ti and Ti5Mo95 alloys. Density functional theory (DFT) and molecular simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. To aid material selection, we present two periodic tables illustrating elemental effects on (a) H2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

cond-mat.mtrl-sci↗

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↗

arXiv2025

Search for stable and low-energy Ce-Co-Cu ternary compounds using machine learning

Cerium-based intermetallics have garnered significant research attention as potential new permanent magnets. In this study, we explore the compositional and structural landscape of Ce-Co-Cu ternary compounds using a machine learning (ML)-guided framework integrated with first-principles calculations. We employ a crystal graph convolutional neural network (CGCNN), which enables efficient screening for promising candidates, significantly accelerating the materials discovery process. With this approach, we predict five stable compounds, Ce3Co3Cu, CeCoCu2, Ce12Co7Cu, Ce11Co9Cu and Ce10Co11Cu4, with formation energies below the convex hull, along with hundreds of low-energy (possibly metastable) Ce-Co-Cu ternary compounds. First-principles calculations reveal that several structures are both energetically and dynamically stable. Notably, two Co-rich low-energy compounds, Ce4Co33Cu and Ce4Co31Cu3, are predicted to have high magnetizations.

cond-mat.mtrl-sci↗

arXiv2024

Giant magnetic anisotropy of Pb atoms in 3d-based magnets

Electronic structure analysis is performed to study the properties of several Pb-containing 3d-intermetallics. Our study reveals that binary metastable Co3Pb and Fe3Pb intermetallic compounds exhibit very attractive intrinsic magnetic properties. We primarily focus on the magnetic anisotropic properties arising from the high spin-orbit coupling of the Pb atom. Decomposing the total anisotropy into intra- and interatomic contributions reveals a significant deviation from single ion anisotropy model with strong symmetric anisotropic pair interactions present. Furthermore, we consider magnetic properties of ternary Pb-based 3d-intermetallics which recently have been reported as stable or metastable. Giant magnetic anisotropy is found on Pb atoms in these systems. The origin of such strong anisotropy in La18Co28Pb3 appears from two sources: spin-orbit and interelectronic Breit couplings. The significance of Breit interaction for magnetic anisotropy in bulk systems has not been reported previously. It is expected that Breit coupling induced anisotropy is dominating in magnetic Pb-based magnets with lower dimensionality including thin films.

cond-mat.mtrl-sci↗

arXiv2024

La$_4$Co$_4$X (X = Pb, Bi, Sb): a demonstration of antagonistic pairs as a route to quasi-low dimensional ternary compounds

We outline how pairs of strongly immiscible elements, referred to here as antagonistic pairs, can be used to synthesize ternary compounds with quasi-reduced dimensional motifs. By identifying third elements that are compatible with a given antagonistic pair, ternary compounds can be formed in which the third element segregates the immiscible atoms into spatially separated substructures. Quasi-low dimensional structural units are a natural consequence of the immiscible atoms seeking to avoid contact in the solid-state. As proof of principle, we present the discovery and physical properties of La$_4$Co$_4$X (X = Pb, Bi, Sb), a new family of intermetallics based on the antagonistic pairs Co-Pb and Co-Bi. La$_4$Co$_4$X adopts a new orthorhombic crystal structure (space group Pbam) containing quasi-2D Co slabs and La-X layers that stack along the a-axis. Consistent with our proposal, the La atoms separate the Co and X substructures, ensuring there are no direct contacts between immiscible atoms. Within the Co slabs, the atoms occupy the vertices of corner sharing tetrahedra and triangles, and this motif produces flat electronic bands near the Fermi level that favor magnetism. The Co is moment bearing in La$_4$Co$_4$X, and we show that whereas La$_4$Co$_4$Pb behaves as a three dimensional antiferromagnet with T$_N$ = 220 K, La$_4$Co$_4$Bi and La$_4$Co$_4$Sb have behavior consistent with low dimensional magnetic coupling and ordering, with T$_N$ = 153 K and 143 K respectively. In addition to the Pb, Bi, and Sb based La$_4$Co$_4$X compounds, we were likely able to produce an analogous La$_4$Co$_4$Sn in polycrystalline form, although we were unable to isolate single crystals. We anticipate that using mutually compatible third elements with an antagonistic pair represents a generalizable design principle for discovering new materials and structure types containing low-dimensional substructures.

cond-mat.mtrl-sci↗

arXiv2024

Coexistence of low and high spin states in La$_{18}$Co$_{28}$Pb$_{3}$

The electronic structure and magnetic properties of a newly predicted stable ternary compound La$_{18}$Co$_{28}$Pb$_{3}$ are studied using electronic structure analysis. The ground state of this compound is ferromagnetic, with three positions of nonequivalent magnetic Co atoms. A strong dependence of magnetic properties on volume shows that this system is situated near the point of magnetic instability. A coexistence of high- and low-spin ferromagnetic states as a function of volume near equilibrium was discovered. A corresponding spin tunneling splitting was estimated. The stability of the theoretically predicted magnetic ground state was tested by varying the Hubbard parameter. The thermal spin fluctuations were added to estimate the paramagnetic moment and a Curie temperature. The necessity of experimental verification of the obtained results is emphasized.

cond-mat.mtrl-sci↗

arXiv2022

Stacking up electron-rich and electron-deficient monolayers to achieve extraordinary mid- to far-infrared excitonic absorption: Interlayer excitons in the C3B/C3N bilayer

Our ability to efficiently detect and generate far-infrared (i.e., terahertz) radiation is vital in areas spanning from biomedical imaging to interstellar spectroscopy. Despite decades of intense research, bridging the terahertz gap between electronics and optics remains a major challenge due to the lack of robust materials that can efficiently operate in this frequency range, and two-dimensional (2D) type-II heterostructures may be ideal candidates to fill this gap. Herein, using highly accurate many-body perturbation theory within the GW plus Bethe-Salpeter equation approach, we predict that a type-II heterostructure consisting of an electron rich C3N and an electron deficient C3B monolayers can give rise to extraordinary optical activities in the mid- to far-infrared range. C3N and C3B are two graphene-derived 2D materials that have attracted increasing research attention. Although both C3N and C3B monolayers are moderate gap 2D materials, and they only couple through the rather weak van der Waals interactions, the bilayer heterostructure surprisingly supports extremely bright, low-energy interlayer excitons with large binding energies of 0.2 ~ 0.4 eV, offering an ideal material with interlayer excitonic states for mid-to far-infrared applications at room temperature. We also investigate in detail the properties and formation mechanism of the inter- and intra-layer excitons.

cond-mat.mtrl-sci↗