4 papers · 1 filter
Reinforcement Fine-Tuning for Materials Design
Zhendong Cao, Lei Wang
Reinforcement fine-tuning played an instrumental role in enhancing the instruction-following and reasoning abilities of large language models. In this work, we employ reinforcement…
CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction
Zhendong Cao, Shigang Ou, Lei Wang
Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driv…
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…
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…