arXiv subjects

Caiyuan Ye

Publications and source records attributed to Caiyuan Ye.

4 recordsLinked to original sources

arXiv2026

A Catalogue of Topological Moiré Bands in Twisted Semiconductors

Twisted two-dimensional semiconductors provide a route to flat and topological moiré minibands, but systematic principles for organizing their material dependence have remained unclear. Here, we establish a high-throughput framework that integrates structural relaxation, first-principles electronic structure calculations, and moiré band topology. We apply this framework to 43 experimentally realized monolayers and 91 symmetry-inequivalent bilayer prototypes, yielding over 1,000 angle-resolved moiré electronic band structures. This database reveals that the low-energy moiré electronic structure is organized primarily by the valley character of the parent band edge together with stacking symmetry. In $Γ$-valley systems, the miniband width usually follows a nearly quadratic twist-angle scaling, consistent with a folding-dominated kinetic-energy scale. In $K$-valley systems, stacking-controlled interlayer hybridization governs whether parent Berry curvature is redistributed into isolated valley Chern minibands. By contrast, $M$-valley systems form a more material-specific class associated with anisotropic and symmetry-constrained band folding. The same valley-and-stacking hierarchy rationalizes the emergence or suppression of $\mathbb{Z}_2$ minibands, and surface termination in Janus bilayers provides a microscopic knob for changing the relevant valley character. These results establish a materials-level organizing principle for designing flat and topological moiré bands in twisted semiconductors.

cond-mat.mtrl-sci↗

arXiv2026

Relative hybridization textures as local coordinates for band geometry and topology

Global diagnostics such as Berry curvature and quantum metrics characterize the geometry and topology of an occupied Bloch subspace, leaving the microscopic sectors that carry this structure implicit. We introduce the relative hybridization coordinate $Z$ as a projector-level diagnostic connecting these global quantities to local degrees of freedom. As the Grassmann graph coordinate relative to a chosen sector, $Z$ reconstructs the local projector and retains the phase and matrix orientation absent from ordinary weight or fat-band descriptions. On valid chart patches, its momentum-space texture encodes Berry curvature, quantum metric, Berry phases, and Wilson loops, while chart obstructions appear as rank-drop defects whose balanced-chart winding of $\det Z$ gives the first Chern number. In the QWZ model this defect inventory reproduces the Chern phase diagram. In the lattice BHZ model, matrix $Z$ diagnoses the orbital $E|H$ partition as a robust matched chart for the QSH geometry, while the spin partition remains essential to the block and $\mathbb Z_2$ interpretation and shows rank deficiency as a matched chart in the spin-conserving limit. The relative hybridization coordinate thus provides a sector-resolved framework for relating band geometry and topology to microscopic structure.

cond-mat.mes-hall↗

arXiv2025

VASPilot: MCP-Facilitated Multi-Agent Intelligence for Autonomous VASP Simulations

Density-functional-theory (DFT) simulations with the Vienna Ab initio Simulation Package (VASP) are indispensable in computational materials science but often require extensive manual setup, monitoring, and postprocessing. Here, we introduce VASPilot, an open-source platform that fully automates VASP workflows via a multi-agent architecture built on the CrewAI framework and a standardized Model Context Protocol (MCP). VASPilot's agent suite handles every stage of a VASP study-from retrieving crystal structures and generating input files to submitting Slurm jobs, parsing error messages, and dynamically adjusting parameters for seamless restarts. A lightweight Flask-based web interface provides intuitive task submission, real-time progress tracking, and drill-down access to execution logs, structure visualizations, and plots. We validate VASPilot on both routine and advanced benchmarks: automated band-structure and density-of-states calculations (including on-the-fly symmetry corrections), plane-wave cutoff convergence tests, lattice-constant optimizations with various van der Waals corrections, and cross-material band-gap comparisons for transition-metal dichalcogenides. In all cases, VASPilot completed the missions reliably and without manual intervention. Moreover, its modular design allows easy extension to other DFT codes simply by deploying the appropriate MCP server. By offloading technical overhead, VASPilot enables researchers to focus on scientific discovery and accelerates high-throughput computational materials research.

cond-mat.mtrl-sci↗

arXiv2025

Materials discovery acceleration by using condition generative methodology

With the rapid advancement of AI technologies, generative models have been increasingly employed in the exploration of novel materials. By integrating traditional computational approaches such as density functional theory (DFT) and molecular dynamics (MD), existing generative models, including diffusion models and autoregressive models, have demonstrated remarkable potential in the discovery of novel materials. However, their efficiency in goal-directed materials design remains suboptimal. In this work we developed a highly transferable, efficient and robust conditional generation framework, PODGen, by integrating a general generative model with multiple property prediction models. Based on PODGen, we designed a workflow for the high-throughput crystals conditional generation which is used to search new topological insulators (TIs). Our results show that the success rate of generating TIs using our framework is 5.3 times higher than that of the unconstrained approach. More importantly, while general methods rarely produce gapped TIs, our framework succeeds consistently, highlighting an effectively $\infty$ improvement. This demonstrates that conditional generation significantly enhances the efficiency of targeted material discovery. Using this method, we generated tens of thousands of new topological materials and conducted further first-principles calculations on those with promising application potential. Furthermore, we identified promising, synthesizable topological (crystalline) insulators such as CsHgSb, NaLaB$_{12}$, Bi$_4$Sb$_2$Se$_3$, Be$_3$Ta$_2$Si and Be$_2$W.

cond-mat.mtrl-sci↗