arXiv subjects

Zhenyao Fang

Publications and source records attributed to Zhenyao Fang.

11 recordsLinked to original sources

arXiv2026

A Multi-Scale Machine Learning Framework for Coupled Chemical, Spin, and Structural Disorder in Alloys

Understanding the thermodynamic properties of disordered magnetic alloys requires a unified treatment of configurational (chemical, spin, etc.) and structural degrees of freedom, which has remained beyond the scope of existing computational frameworks. Here we present a general framework that integrates machine learning models (such as graph neural networks and machine learning interatomic potentials) and statistical sampling methods (such as Monte Carlo and molecular dynamics simulations) to study the coupled chemical, spin, and structural disorder in alloys. We demonstrate the framework on body-centered-cubic Fe-Co alloys with interstitial carbon dopants, where the Fe-Co host exhibits intrinsic chemical and spin disorder, and the interstitial carbon introduces additional structural disorder through local lattice distortions, making the system a prototypical multi-disorder magnetic alloy. The framework predicts the order-to-disorder phase transition temperature and the melting temperature of Fe-Co alloy to be 1,000 K and 1,690 K, in excellent agreement with the experimentally measured values of 1,006 K and approximately 1,700 K, respectively. It also predicts the tetragonal-to-nearly-cubic structural transitions in Fe-Co-C alloy as temperature increases. These results establish the framework as a reliable tool for studying multi-disorder alloys, with applications to complex disordered systems such as high-entropy alloys, multiferroics, and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Roadmap: 2D Materials for Quantum Technologies

Two-dimensional (2D) materials have emerged as a versatile and powerful platform for quantum technologies, offering atomic-scale control, strong quantum confinement, and seamless integration into heterogeneous device architectures. Their reduced dimensionality enables unique quantum phenomena, including optically addressable spin defects, tunable single-photon emitters, low-dimensional magnetism, gate-controlled superconductivity, and correlated states in Moir\'e superlattices. This Roadmap provides a comprehensive overview of recent progress and future directions in exploiting 2D materials for quantum sensing, computation, communication, and simulation. We survey advances spanning spin defects and quantum sensing, quantum emitters and nonlinear photonics, computational theory and data-driven discovery of quantum defects, spintronic and magnonic devices, cavity-engineered quantum materials, superconducting and hybrid quantum circuits, quantum dots, Moir\'e quantum simulators, and quantum communication platforms. Across these themes, we identify common challenges in defect control, coherence preservation, interfacial engineering, and scalable integration, alongside emerging opportunities driven by machine$-$learning$-$assisted design and integrated experiment$-$theory feedback loops. By connecting microscopic quantum states to mesoscopic excitations and macroscopic device architectures, this Roadmap outlines a materials-centric framework for integrating coherent quantum functionalities and positions 2D materials as foundational building blocks for next-generation quantum technologies.

quant-ph↗

arXiv2025

Ideal Topological Flat Bands in Two-dimensional Moir\'e Heterostructures with Type-II Band Alignment

Topological flat bands play an essential role in inducing exotic interacting physics, ranging from fractional Chern insulators to superconductivity, in moir\'e materials. In this work, we propose a design principle for realizing topological flat bands with "ideal quantum geometry", namely the trace of Fubini-Study metric equals to the Berry curvature, in a class of two-dimensional moir\'e heterostructures with type-II band alignment. We first introduce a moir\'e Chern-band model to describe this system and show that topological flat bands can be realized in this model when the moir\'e superlattice potential is stronger than the type-II atomic band gap of the heterostructure. Next, we map this model into a topological heavy fermion model that consists of a localized orbital for "f-electron" and a conducting band for "c-electron". We find that both the flatness and quantum geometry of the flat band in the topological heavy fermion model depend on the energy gap between c-electron and f-electron bands at $\Gamma$ which is experimentally controllable via external gate voltages. This tunability will allow us to realize an ideal topological flat band with zero band-width and ideal quantum geometry. Our design strategy of topological flat bands is insensitive of twist angle. We also discuss possible material candidates for moir\'e heterostructures with type-II band alignment.

cond-mat.mes-hall↗

arXiv2025

Coherent Spins in van der Waals Semiconductor GeS2 at Ambient Conditions

Optically active spin defects in van der Waals (vdW) materials have recently emerged as versatile quantum sensors, enabling applications from nanoscale magnetic field detection to the exploration of novel quantum phenomena in condensed matter systems. Their ease of exfoliation and compatibility with device integration make them promising candidates for future quantum technologies. Here we report the observation and room-temperature coherent control of spin defects in the high-temperature crystalline phase of germanium disulfide ($\beta$-GeS2), a two-dimensional (2D) semiconductor with low nuclear spin density. The observed spin defects exhibit spin-1/2 behavior, and their spin dynamics can be explained by a weakly coupled spin-pair model. We implement dynamical decoupling techniques to extend the spin coherence time (T$_2$) by a factor of 20. Finally, we use density functional theory (DFT) calculations to estimate the structure and spin densities of two possible spin defect candidates. This work will help expand the field of quantum sensing with spin defects in van der Waals materials.

quant-ph↗

arXiv2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the computational cost of sampling the large supercell and configurational space. The recent development of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we introduce a computational framework that integrates GNNs with Monte Carlo simulations for efficient calculations of thermodynamic properties and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer \ch{Ti3C2T}$_{2-x}$ as a representative system, we investigate the effect of surface termination disorder involving \ch{-F}, \ch{-O}, and termination vacancies on the electrical and optical conductivity spectra. We find that surface termination disorder affects the temperature dependence of electrical conductivity, inducing a peak close to the order-disorder phase transition temperature that reflects the competition between scattering and electron filling effects of the surface termination groups across the phase transition. In contrast, optical conductivity remains robust to local disorder across a wide temperature range and is governed primarily by the global chemical composition of surface terminations. These results demonstrate the utility of our machine-learning-assisted framework for statistically modeling disorder effects and ensemble properties in complex materials, opening new avenues for future studies of disorder-driven phenomena in systems such as high-entropy alloys and disordered magnetic compounds.

cond-mat.mtrl-sci↗

arXiv2025

Accurate Prediction of Tensorial Spectra Using Equivariant Graph Neural Network

Optical spectroscopies provide a powerful tool for harnessing light-matter interactions for unraveling complex electronic features such as the flat bands and nontrivial topologies of materials. These insights are crucial for the development and optimization of optoelectronic devices, including solar cells, light-emitting diodes, and photodetectors, where device performance is closely connected with the nature of the underlying electronic spectrum. Realistic modeling of tensor optical responses in materials, which are computationally quite demanding, however, remains challenging. Here we introduce the Tensorial Spectra Equivariant Neural Network (TSENN), which is a equivariant graph neural network architecture that maps crystal structures directly to their full photon-frequency-dependent optical tensors. By encoding the isotropic sequential scalar components along with the anisotropic sequential tensor components into l = 0 and l = 2 spherical tensor components, TSENN ensures symmetry-aware predictions that are consistent with the constraints of crystalline symmetries of materials. Trained on a dataset of frequency-dependent permittivity tensors of 1,432 bulk semiconductors computed using first-principles methods, our model achieves a mean absolute error (MAE) of 21.181 millifarads per meter (mF/m), demonstrating its potential for efficient modeling of other related properties such as the optical conductivities. Our framework opens new avenues for rational data-driven design of anisotropic optical responses for accelerating materials discovery for advancing optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Database of Tensorial Optical and Transport Properties of Materials From the Wannier Function Method

The discovery and design of functional materials for energy harvesting and electronic applications require accurate predictions of their optical and transport properties. While several existing databases contain the first-order optical properties and the electron transport properties calculated from high-throughput first-principles calculations, the amount of material entries is often limited and those functional properties are often reported in scalar form. Comprehensive databases for the tensorial properties still remain inadequate, which prevents from capturing the anisotropic effect in materials and the development of advanced machine learning models that incorporate the space group symmetry of materials. Therefore, in this work we present the largest-to-date database of tensorial optical properties (optical conductivity, shift current) and the database of tensorial transport properties (electrical conductivity, thermal conductivity, Seebeck coefficient, thermoelectric figure of merit zT) for 7301 materials, calculated from the Wannier function method. The quality of the Wannier functions were validated by the maximal spread of the Wannier functions and by the comparison with the band structures from first-principles calculations, ensuring the accuracy of the calculated properties. These results contribute to the systematic study the functional properties for diverse materials and can benefit future data-driven discovery of candidate materials for optoelectronic and thermoelectric applications.

cond-mat.mtrl-sci↗

arXiv2024

Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks

In machine-learning-assisted high-throughput defect studies, a defect-aware latent representation of the supercell structure is crucial to the accurate prediction of defect properties. The performance of current graph neural network (GNN) models is limited due to the fact that defect properties depend strongly on the local atomic configurations near the defect sites and due to the over-smoothing problem of GNN. Herein, we demonstrate that persistent homology features, which encode the topological information of local chemical environment around each atomic site, can characterize the structural information of defects. Using the dataset containing a wide spectrum of \ch{O}-based perovskites with all available vacancies as an example, we show that incorporating the persistent homology features, along with proper choices of graph pooling operations, significantly increases the prediction accuracy, with the MAE reduced by 55\%. Those features can be easily integrated into the state-of-the-art GNN models, including the graph Transformer network and the equivariant neural network, and universally improve their performance. Besides, our model also overcomes the convergence issue with respect to the supercell size that was present in previous GNN models. Furthermore, using the datasets of defective \ch{BaTiO3} with multiple substitutions and multiple vacancies as examples, our GNN model can also predict the defect-defect interactions accurately. These results suggest that persistent homology features can effectively improve the performance of machine learning models and assist the accelerated discovery of functional defects for technological applications.

cond-mat.mtrl-sci↗

arXiv2024

Towards Accurate Prediction of Configurational Disorder Properties in Materials using Graph Neural Networks

The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a new data-driven paradigm for the accelerated design of configurationally-complex functional material systems.

cond-mat.mtrl-sci↗

arXiv2023

Controllable Topological Insulator Phases in Litharge-phase InBi Monolayer

Despite recent advances of layered square-net topological material models that possess ideal semimetallic electronic structures and promising potential in material applications, the identification of experimentally accessible two-dimensional square-net materials with related topological properties has proven challenging. Due to the highly tunable physical and topological properties of III-V semiconductors, we revisit the class of III-V materials and observe that the litharge-phase InBi is a layered square-net material and can be exfoliated into the InBi monolayer. We present a comprehensive first-principles study of the energy landscape of the InBi monolayer. We identify a paraelastic phase and three ferroelastic phases and study their topological properties. Specifically, we show that the paraelastic InBi monolayer is a trivial insulator due to the orbital-ordering-induced band inversion occurring between states with the same parity. Substituting one Bi atom per cell with another V-group element (N, P, As) or applying an electric field that breaks the inversion symmetry and changes the orbital onsite energy, the paraelastic InBi monolayer can be driven into the topological insulator phase. Furthermore, one of the ferroelastic phases of pure InBi, which can be obtained by gently straining the paraelastic phase, also possesses such topological insulating properties. These results provide several experimentally accessible routes to tune the nontrivial topology in the InBi monolayer, including creating heterostructures with piezoelectric or ferroelectric substrates and applying mechanical strain, making the InBi monolayer an ideal platform to study the interplay of reduced dimensionality, square-net chemical bonding networks, and band topology.

cond-mat.mtrl-sci↗

arXiv2019

Ideal near-Dirac triple-point semimetal in III-V semiconductor alloys

Despite the growing interest in topological materials, the difficulty of experimentally synthesizing and integrating them with other materials has been one of the main barriers restricting access to their unique properties. Recent advances in synthesizing metastable phases of crystalline materials can help to overcome this barrier and offer new platforms to experimentally study and manipulate band topology. Because III-V semiconductors have a wide range of functional material applications (including optoelectronic devices, light-emitting diodes, and highly efficient solar cells), and because Bi-doped III-V materials can be synthesized by ion plantation and ion-cutoff methods, we revisit the effect of bismuth substitution in metastable III-V semiconductors. Through first-principles calculation methods, we show that in wurtzite structure III-V materials, Bi substitution can lead to band inversion phenomena and induce nontrivial topological properties. Specifically, we identify that GaBi and InBi are Dirac-Weyl semimetals, characterized by the coexistence of Dirac points and Weyl points, and $\text{GaAs}_{0.5} \text{Bi}_{0.5}$, $\text{GaSb}_{0.5} \text{Bi}_{0.5}$, $\text{InSb}_{0.5} \text{Bi}_{0.5}$ are triple-point semimetals, characterized by two sets of "near Dirac" triple points on the Fermi level. These experimentally-accessible bismuth-based topological semimetals can be integrated into the large family of functional III-V materials for experimental studies of heterostructures and future optoelectronic applications.

cond-mat.mtrl-sci↗