arXiv subjects

Haosheng Xu

Publications and source records attributed to Haosheng Xu.

5 recordsLinked to original sources

arXiv2026

Machine learning reveals common features of unconventional superconductors with high transition temperatures

Superconductors with high critical temperatures that emerges beyond the phonon-mediated regime are usually considered unconventional in nature, yet unlike conventional superconductors, no broadly applicable predictive theory currently guides their discovery. Here, we use interpretable machine learning to uncover a common materials-space signature of high-$T_{\mathrm{c}}$ unconventional superconductors and develop a data-driven strategy for materials discovery. We construct a unified feature representation for each material by integrating compositional statistics, structural information, and latent representations from trained property-prediction models, followed by structure-aware filtering of an experimentally established superconducting dataset. Without using transition-temperature information, unsupervised analysis shows that cuprate and iron-based superconductors occupy a common region of materials space, characterized primarily by large electronegativity deviation and intermediate mean valence-electron number. A supervised $T_{\text{c}}$ model independently identifies the same descriptors as dominant features, providing complementary evidence for their relevance. Using this empirical materials-space prior together with the $T_{\text{c}}$ model, we prioritize candidate materials, recover recently discovered nickelate superconductors, and identify chemically distinct candidates for future investigation.

cond-mat.supr-con↗

arXiv2025

Isolated nearly flat higher Chern band in monolayer transition metal trihalides

The interplay between non-trivial topology and strong electron interaction can generate a variety of exotic quantum matter. Here we theoretically propose that monolayer transition metal trihalides MoF$_3$ and W$X_3$ ($X$= Cl, Br, I) have isolated nearly flat band near the Fermi level with higher Chern number $\mathcal{C}=+3$ and $\mathcal{C}=-2$, respectively. The nontrivial topology of these flat Chern bands originates from the effective $sd^2$ hybridization of transition metal atom, which transform the apparent atomic $d$ orbitals on a hexagonal lattice into $(s, p_+, p_-)$ orbitals on a triangular lattice. Interestingly, the quantum geometry of flat Chern bands in these materials are comparable with those in moiré systems exhibiting fractional Chern insulator state. The Hofstadter butterfly of such flat Chern bands are further studied. These natural materials, if realized experimentally, could offer new platforms to explore correlated phenomena driven by flat Chern band with higher Chern number.

cond-mat.mes-hall↗

arXiv2025

Design Topological Materials by Reinforcement Fine-Tuned Generative Model

Topological insulators (TIs) and topological crystalline insulators (TCIs) are materials with unconventional electronic properties, making their discovery highly valuable for practical applications. However, such materials, particularly those with a full band gap, remain scarce. Given the limitations of traditional approaches that scan known materials for candidates, we focus on the generation of new topological materials through a generative model. Specifically, we apply reinforcement fine-tuning (ReFT) to a pre-trained generative model, thereby aligning the model's objectives with our material design goals. We demonstrate that ReFT is effective in enhancing the model's ability to generate TIs and TCIs, with minimal compromise on the stability of the generated materials. Using the fine-tuned model, we successfully identify a large number of new topological materials, with Ge$_2$Bi$_2$O$_6$ serving as a representative example--a TI with a full band gap of 0.26 eV, ranking among the largest known in this category.

cond-mat.mtrl-sci↗

arXiv2024

Predicting Many Crystal Properties via an Adaptive Transformer-based Framework

Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We introduce CrystalBERT, an adaptable transformer-based framework integrating space group, elemental, and unit cell information. This novel structure can seamlessly combine diverse features and accurately predict various physical properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT provides insightful interpretations of features influencing target properties. Our results indicate that space group and elemental information are crucial for predicting topological and superconducting properties, underscoring their intricate nature. By incorporating these features, we achieve 91\% accuracy in topological classification, surpassing prior studies and identifying previously misclassified materials. This research demonstrates that integrating diverse material information enhances the prediction of complex material properties, paving the way for more accurate and interpretable machine learning models in materials science.

cond-mat.mtrl-sci↗

arXiv2023

Discovering two-dimensional magnetic topological insulators by machine learning

Topological materials with unconventional electronic properties have been investigated intensively for both fundamental and practical interests. Thousands of topological materials have been identified by symmetry-based analysis and ab initio calculations. However, the predicted magnetic topological insulators with genuine full band gaps are rare. Here we employ this database and supervisedly train neural networks to develop a heuristic chemical rule for electronic topology diagnosis. The learned rule is interpretable and diagnoses with a high accuracy whether a material is topological using only its chemical formula and Hubbard $U$ parameter. We next evaluate the model performance in several different regimes of materials. Finally, we integrate machine-learned rule with ab initio calculations to high-throughput screen for magnetic topological insulators in 2D material database. We discover 6 new classes (15 materials) of Chern insulators, among which 4 classes (7 materials) have full band gaps and may motivate for experimental observation. We anticipate the machine-learned rule here can be used as a guiding principle for inverse design and discovery of new topological materials.

cond-mat.mtrl-sci↗