72 citations · 108 across the 8 of their papers we have counts for
10 papers
Screening phonon-mediated superconductors from static orbital Hamiltonians
Jian-Feng Zhang, Ze-Feng Gao, Xiao-Qi Han +6
The first-principles search for superconductors is severely limited by the high cost of electron-phonon coupling (EPC) calculations. Here we develop a low-cost, physically transpar…
PhononScore: a phonon-aware scoring function for dynamical stability
Xiao-Qi Han, Ze-Feng Gao, Zhong-Yi Lu
In recent years, crystal generation models have enabled the design of massive numbers of candidate materials. However, the lack of dynamical stability among generated structures ha…
NQS-Agent: Health-Aware Agentic Hyperparameter Optimization for Neural-Network Quantum States
Jia-Qi Wang, Xiao-Qi Han, Ze-Feng Gao +2
Neural-network quantum states (NQS) provide expressive variational representations for strongly correlated quantum many-body systems, but their practical accuracy depends sensitive…
Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors
Jian-Feng Zhang, Ze-Feng Gao, Xiao-Qi Han +7
Although chemical bonding is the fundamental mechanistic bridge connecting atomic structure to macroscopic material properties, current data-driven materials science largely treats…
PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation
Xiao-Qi Han, Ze-Feng Gao, Wen-Kao Li +2
In recent years, generative artificial intelligence has made significant advances in the design of crystalline materials, giving rise to approaches based on graph neural networks,…
Superconductivity in atom-intercalated quaternary hydrides under ambient pressure
Bo-Wen Yao, Zhenfeng Ouyang, Xiao-Qi Han +4
Hydrogen-rich materials are the most promising candidates for high-temperature conventional superconductors under ambient pressure. Multinary hydrides have abundant structural conf…
HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
Xiao-Qi Han, Ze-Feng Gao, Xin-De Wang +3
The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting tran…
InvDesFlow-AL: active learning-based workflow for inverse design of functional materials
Xiao-Qi Han, Peng-Jie Guo, Ze-Feng Gao +2
Developing inverse design methods for functional materials with specific properties is critical to advancing fields like renewable energy, catalysis, energy storage, and carbon cap…
High-temperature superconductivity in LiAuH mediated by strong electron-phonon coupling under ambient pressure
Zhenfeng Ouyang, Bo-Wen Yao, Xiao-Qi Han +3
We used our developed AI search engine~(InvDesFlow) to perform extensive investigations regarding ambient stable superconducting hydrides. A cubic structure LiAuH with Au-H…
AI-driven inverse design of materials: Past, present and future
Xiao-Qi Han, Xin-De Wang, Meng-Yuan Xu +5
The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essential…