72 citations · 93 across the 5 of their papers we have counts for
4 papers · 1 filter
CrystalFlow: A Flow-Based Generative Model for Crystalline Materials
Xiaoshan Luo, Zhenyu Wang, Qingchang Wang +4
Deep learning-based generative models have emerged as powerful tools for modeling complex data distributions and generating high-fidelity samples, offering a transformative approac…
Data-driven design of high-temperature superconductivity among ternary hydrides under pressure
Bowen Jiang, Xiaoshan Luo, Toshiaki Iitaka +6
Recently, ternary clathrate hydrides are promising candidates for high-temperature superconductor. However, it is a formidable challenge to effectively hunt high-temperature superc…
Space Group Informed Transformer for Crystalline Materials Generation
Zhendong Cao, Xiaoshan Luo, Jian Lv +1
We introduce CrystalFormer, a transformer-based autoregressive model specifically designed for space group-controlled generation of crystalline materials. By explicitly incorporati…
Deep learning generative model for crystal structure prediction
Xiaoshan Luo, Zhenyu Wang, Pengyue Gao +4
Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in na…