arXiv subjects

Qimin Yan

Publications and source records attributed to Qimin Yan.

At least 19 recordsLinked to original sources

arXiv2026

Point Group Equivariant Graph Neural Networks for Materials

Equivariant graph neural networks have proven effective tools for inference of material's properties directly from their structure. Traditionally, these have been applied such that they respect full $O(3)$ equivariance, so that any rotation or reflection of the input structure is respected in the model's output. While this works for general arrangements of atoms, additional symmetries of atomistic systems are left unleveraged. Furthermore, any symmetries of the filter functions are implicitly learned from the full dataset and not strictly enforced. In this work, we introduce point-group symmetry aware equivariant graph neural networks (PGEqNN) for materials science, with filter functions aligned with symmetry-aware indices for greater granularity in predictive tasks. With this architecture, we show that most of the predictive power of equivariant networks for tensorial elastic and dielectric datasets lies in the trivial subspaces of the point-group adapted bases. Exploiting this, an $A_1$-restricted variant matches or improves on its full point-group and $SO(3)$-partitioned counterparts while training fewer active parameters, yielding leaner models of equal accuracy.

cond-mat.dis-nn↗

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↗

arXiv2026

Materials design based on a material-motif network and heterogeneous graphs

Machine learning models for functional materials design require precise and informative representations of material systems. Common representations encode atomic composition and bonding but often do not include local coordination environments across chemically diverse crystals. Recurring structural motifs provide a motif level description of crystalline solids and can serve as interpretable descriptors for structure property learning. To analyze the motif connectivity in materials, we construct a bipartite material motif network from 131,548 Materials Project entries, with materials and motifs as the two node sets. Edges connect materials to their constituent motifs and are weighted by motif distortion, which quantifies the strength of each material motif association. Network connectivity is analyzed to identify motif-defined material clusters that capture recurring local geometries relevant to structure property trends. Most shared motifs act as hubs that connect otherwise disconnected regions of the network, enabling motif guided screening by expanding from known motifs to nearby materials in the same neighborhoods. A network embedding step converts this weighted connectivity into vector representations of materials. Using these motif informed embeddings, property prediction yields a formation energy mean absolute error (MAE) of 0.157 eV per atom and a bandgap MAE of 0.601 eV. These results indicate that motif connectivity provides a compact, interpretable representation that complements existing descriptors for scalable screening and structure property modeling.

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

Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach

Predicting electronic band structures from crystal structures is crucial for understanding structure-property correlations in materials science. First-principles approaches are accurate but computationally intensive. Recent years, machine learning (ML) has been extensively applied to this field, while existing ML models predominantly focus on band gap predictions or indirect band structure estimation via solving predicted Hamiltonians. An end-to-end model to predict band structure accurately and efficiently is still lacking. Here, we introduce a graph Transformer-based end-to-end approach that directly predicts band structures from crystal structures with high accuracy. Our method leverages the continuity of the k-path and treat continuous bands as a sequence. We demonstrate that our model not only provides accurate band structure predictions but also can derive other properties (such as band gap, band center, and band dispersion) with high accuracy. We verify the model performance on large and diverse datasets.

cond-mat.mtrl-sci↗

arXiv2024

Crystal Hypergraph Convolutional Networks

Graph representations of solid state materials that encode only interatomic distance lack geometrical resolution, resulting in degenerate representations that may map distinct structures to equivalent graphs. Here we propose a hypergraph representation scheme for materials that allows for the association of higher-order geometrical information with hyperedges. Hyperedges generalize edges to connected sets of more than two nodes, and may be used to represent triplets and local environments of atoms in materials. This generalization of edges requires a different approach in graph convolution, three of which are developed in this paper. Results presented here focus on the improved performance of models based on both pair-wise edges and local environment hyperedges. These results demonstrate that hypergraphs are an effective method for incorporating geometrical information in material representations.

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

Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals

Modern E(3)-Equivariant networks may be used to predict rotationally equivariant properties, including tensorial quantities. Three such quantities: the dielectric, piezoelectric, and elasticity tensors, are computationally expensive to produce ab initio for crystalline systems; however, with greater availability of such data in large material property databases, we now have a sufficient target space to begin training equivariant models in the prediction of such properties. Here we explicitly develop spherical harmonic decompositions of these tensorial properties using their general symmetries. We then apply three distinct E(3)-equivariant convolutional structures to the prediction of the components of these decompositions, allowing us to predict the aforementioned tensorial quantities in an equivariant manner and compare performance. We further report results testing the transferability of these predictive models between different tensorial target sets.

physics.comp-ph↗

arXiv2024

Symmetry-guided data-driven discovery of native quantum defects in two-dimensional materials

Drawing on their atomically thin structure, two-dimensional (2D) materials present a groundbreaking avenue for the precision fabrication and systematic manipulation of quantum defects. Through a method grounded in site-symmetry principles, we devise a comprehensive workflow to pinpoint potential native quantum defects across the entire spectrum of known binary 2D materials. Leveraging both symmetry principles and data-driven approaches markedly enhances the identification of spin defects exhibiting triplet ground states. This advancement is pivotal in discovering NV-like quantum defects in 2D materials, which are instrumental in facilitating a set of quantum functionalities. For discerning the multifaceted functionalities of these quantum defect candidates, their magneto-optical properties are comprehensively estimated using high-throughput computations. Our findings underscore that antisite defects in diverse hosts emerge as prospective quantum defects of significance. Crucially, based on our research, we advocate that the 16 antisites present in post-transition metal monochalcogenides (PTMCs) stand out as a prominent 2D-material-based quantum defect platforms, by their precise defect levels, optimal magneto-optical attributes, and the readily accessible nature of their host materials. This work substantially broadens the repertoire of quantum defects within the 2D material landscape, presenting profound implications for the advancement of quantum information science and technologies.

cond-mat.mtrl-sci↗

arXiv2024

Bridging deep learning force fields and electronic structures with a physics-informed approach

This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional large-scale material systems. The deep potential molecular dynamics model is adopted as the backbone, and electronic structure simulation is integrated. Using Wannier functions as the basis, we categorize Wannier Hamiltonian elements based on physical principles to incorporate diverse information from a deep-learning force field model. This information-sharing mechanism streamlines the architecture of our multifunctional model, enhancing its efficiency and effectiveness. Utilizing Wannier functions as the basis lays the groundwork for predicting more physical quantities. This approach serves as a powerful tool to explore both the structural and electronic properties of large-scale systems characterized by low periodicities. By endowing an existing well-developed machine-learning force field with electronic structure simulation capabilities, the study marks a significant advancement in developing multimodal machine-learning-based computational methods that can achieve multiple functionalities traditionally exclusive to first-principles calculations.

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

Bilayer Kagome Borophene with Multiple van Hove Singularities

The appearance of van Hove singularities near the Fermi level leads to prominent phenomena, including superconductivity, charge density wave, and ferromagnetism. Here a bilayer Kagome lattice with multiple van Hove singularities is designed and a novel borophene with such lattice (BK-borophene) is proposed by the first-principles calculations. BK-borophene, which is formed via three-center two-electron (3c-2e) sigma-type bonds, is predicted to be energetically, dynamically, thermodynamically, and mechanically stable. The electronic structure hosts both conventional and high-order van Hove singularities in one band. The conventional van Hove singularity resulting from the horse saddle is 0.065 eV lower than the Fermi level, while the high-order one resulting from the monkey saddle is 0.385 eV below the Fermi level. Both the singularities lead to the divergence of electronic density of states. Besides, the high-order singularity is just intersected to a Dirac-like cone, where the Fermi velocity can reach 1340000 m/s. The interaction between the two Kagome lattices is critical for the appearance of high-order van Hove singularities. The novel bilayer Kagome borophene with rich and intriguing electronic structure offers an unprecedented platform for studying correlation phenomena in quantum material systems and beyond.

cond-mat.mtrl-sci↗

arXiv2023

Exciton Confinement in Two-Dimensional, In-Plane, Quantum Heterostructures

Two-dimensional (2D) semiconductors are promising candidates for optoelectronic application and quantum information processes due to their inherent out-of-plane 2D confinement. In addition, they offer the possibility of achieving low-dimensional in-plane exciton confinement, similar to zero-dimensional quantum dots, with intriguing optical and electronic properties via strain or composition engineering. However, realizing such laterally confined 2D monolayers and systematically controlling size-dependent optical properties remain significant challenges. Here, we report the observation of lateral confinement of excitons in epitaxially grown in-plane MoSe2 quantum dots (~15-60 nm wide) inside a continuous matrix of WSe2 monolayer film via a sequential epitaxial growth process. Various optical spectroscopy techniques reveal the size-dependent exciton confinement in the MoSe2 monolayer quantum dots with exciton blue shift (12-40 meV) at a low temperature as compared to continuous monolayer MoSe2. Finally, single-photon emission was also observed from the smallest dots at 1.6 K. Our study opens the door to compositionally engineered, tunable, in-plane quantum light sources in 2D semiconductors.

cond-mat.mes-hall↗