arXiv subjects

Shuo Tao

Publications and source records attributed to Shuo Tao.

7 recordsLinked to original sources

arXiv2026

High-throughput Discovery of Magnetic Rare Earth Transition Metal Alloys

We present an accelerated materials discovery framework that combines diffusion-based crystal structure generation with hierarchical screening to identify new rare-earth--transition-metal magnets simultaneously achieving high magnetization and thermodynamic stability. Using this workflow, we systematically explored over 3000 binary (R-T) and ternary (R-T-T$'$) compositions spanning R~$\in \{\text{Y, Sm}\}$, T~$\in \{\text{Fe, Co, Ni}\}$, and T$' \in \{\text{Ti, V, Cr, Mn, Cu, Zn}\}$, and filtered approximately 240{,}000 generated crystal structures through machine-learning interatomic potential prescreening and spin-polarized density functional theory validation. We identify 300+ low-energy magnetic candidates within 0.1~eV/atom above the convex hull at the DFT level, including 5 thermodynamically stable phases. The highest saturation magnetization reaches ${\sim}1.8$~T in Fe-rich binary and ternary phases (SmFe$_{12}$, YFe$_{12}$, YFe$_{18}$Ti and Sm$_2$Fe$_{16}$Mn). Symmetry analysis reveals that the majority of ternary candidates are subgroup derivatives of known binary prototypes through Wyckoff site splitting that accommodates T$'$ substitution. Site-resolved magnetic moment analysis further shows that Mn aligns ferromagnetically with the Fe sublattice with minimal magnetization loss, whereas Cr couples antiferromagnetically, providing systematic guidance for dopant selection. These findings demonstrate a generalizable strategy for targeted magnetic materials discovery and suggest that extending generative searches to larger unit cells ($>$20 atoms) with higher Fe fractions is a promising route toward stable phases with saturation magnetization exceeding 1.8~T.

cond-mat.mtrl-sci↗

arXiv2026

Accelerated Inorganic Electrides Discovery by Generative Models and Hierarchical Screening

Electrides are exotic compounds in which excess electrons occupy interstitial regions of the crystal lattice and serve as anions, exhibiting exceptional properties such as low work function, high electron mobility, and strong catalytic activity. Although they show promise for diverse applications, identifying new electrides remains challenging due to the difficulty of achieving energetically favorable electron localization in crystal cavities. Here, we present an accelerated materials discovery framework that combines physical principles, diffusion-based materials generation with hierarchical thermodynamic and electronic structure screening. Using this workflow, we systematically explored 1,510 binary and 6,654 ternary chemical compositions containing excess valence electrons from electropositive alkaline, alkaline-earth, and early transition metals, and then filtered them with a high throughput validation on both thermodynamical stability and electronic structure analysis. As a result, we have identified 264 new electron rich compounds within 0.05 eV/atom above the convex hull at the density functional theory (DFT) level, including 13 thermodynamically stable electrides. Our approach demonstrates a generalizable strategy for targeted materials discovery in a vast chemical space.

cond-mat.mtrl-sci↗

arXiv2024

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties

Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments. The model demonstrates superior performance across various materials property prediction tasks, achieving particularly notable results in properties sensitive to many-body interactions. For band gap prediction, EOSnet achieves a mean absolute error of 0.163 eV, surpassing previous state-of-the-art models. The model also excels in predicting mechanical properties and classifying materials, with 97.7\% accuracy in metal/non-metal classification. These results demonstrate that embedding GOM fingerprints into node features enhances the ability of GNNs to capture complex atomic interactions, making EOSnet a powerful tool for materials discovery and property prediction.

cond-mat.mtrl-sci↗

arXiv2024

Toward Motion Robustness: A masked attention regularization framework in remote photoplethysmography

There has been growing interest in facial video-based remote photoplethysmography (rPPG) measurement recently, with a focus on assessing various vital signs such as heart rate and heart rate variability. Despite previous efforts on static datasets, their approaches have been hindered by inaccurate region of interest (ROI) localization and motion issues, and have shown limited generalization in real-world scenarios. To address these challenges, we propose a novel masked attention regularization (MAR-rPPG) framework that mitigates the impact of ROI localization and complex motion artifacts. Specifically, our approach first integrates a masked attention regularization mechanism into the rPPG field to capture the visual semantic consistency of facial clips, while it also employs a masking technique to prevent the model from overfitting on inaccurate ROIs and subsequently degrading its performance. Furthermore, we propose an enhanced rPPG expert aggregation (EREA) network as the backbone to obtain rPPG signals and attention maps simultaneously. Our EREA network is capable of discriminating divergent attentions from different facial areas and retaining the consistency of spatiotemporal attention maps. For motion robustness, a simple open source detector MediaPipe for data preprocessing is sufficient for our framework due to its superior capability of rPPG signal extraction and attention regularization. Exhaustive experiments on three benchmark datasets (UBFC-rPPG, PURE, and MMPD) substantiate the superiority of our proposed method, outperforming recent state-of-the-art works by a considerable margin.

cs.CV↗

arXiv2024

Accelerating Structural Optimization through Fingerprinting Space Integration on the Potential Energy Surface

Structural optimization has been a crucial component in computational materials research, and structure predictions have relied heavily on this technique in particular. In this study, we introduce a novel method that enhances the efficiency of local optimization by integrating an extra fingerprint space into the optimization process. Our approach utilizes a mixed energy concept in the hyper potential energy surface (PES), combining real energy and a newly introduced fingerprint energy derived from the symmetry of local atomic environment. This method strategically guides the optimization process toward high-symmetry, low-energy structures by leveraging the intrinsic symmetry of atomic configurations. The effectiveness of our approach was demonstrated through structural optimizations of silicon, silicon carbide, and Lennard-Jones cluster systems. Our results show that the fingerprint space biasing technique significantly enhances the performance and probability of discovering energetically favorable, high-symmetry structures, as compared to conventional optimizations. The proposed method is anticipated to streamline the search for new materials and facilitate the discovery of novel, energetically favorable configurations.

cond-mat.mtrl-sci↗

arXiv2016

The critical temperature of smart meta-superconducting MgB2

Enhancing the critical temperature (TC) is important not only to the practical applications but also to the theories of superconductivity. MgB2 is a type II superconductor with a TC of 39 K, which is very close to the McMillan limit. Improving the TC of MgB2 is challenging but significant. Inspired by the metamaterial structure, we designed a smart meta-superconductor that consists of MgB2 microparticles and Y2O3:Eu3+ nanorods. In the local electric field, Y2O3:Eu3+ nanorods will generate electroluminescence (EL) that can excite MgB2 particles, thereby improving the TC by strengthening the electron-phonon interaction. Each MgB2-based superconductor doped with one of the four dopants of different EL intensities was prepared by an ex-situ process. The results showed that the addition of Y2O3:Eu3+ brings about an impurity effect that decreases the TC and an EL exciting effect that increases the TC. Apart from the EL intensity, the micro-morphology and degree of dispersion of the dopants also affected the TC. This smart meta-superconductor provides a new method for increasing TC.

cond-mat.supr-con↗

arXiv2015

Improving the critical temperature of MgB2 superconducting metamaterials induced by electroluminescence

The MgB2 superconductor was doped with electroluminescent Y2O3:Eu, to synthesise a superconducting metamaterial. The temperature dependence of the resistivity of the superconductor indicates that the critical temperature (TC) of samples decrease when increasing the amount of doped Y2O3 nanorods, due to impurity (Y2O3, MgO and YB4). However, the TC of the samples increase with increasing amount of doped Y2O3:Eu3+ nanorods, which are opposite to doped Y2O3 nanorods. Moreover, the transition temperature of the sample doped with 8 wt. % Y2O3:Eu3+ nanorods is higher than those of doped and pure MgB2. The TC of the sample doped with 8 wt. % Y2O3:Eu3+ nanorods is 1.15 K higher than that of the sample doped with 8 wt. % Y2O3. The TC of sample doped with 8 wt. % Y2O3:Eu3+ is 0.4 K higher than that of pure MgB2. Results indicate that doping electroluminescent materials into MgB2 increases the transition temperature; this novel strategy may also be applicable to other superconductors.

cond-mat.supr-con↗