1 citations · 1 across the 3 of their papers we have counts for
5 papers
Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization in Atomistic Models
Chengqian Zhang, Duo Zhang, Anyang Peng +7
Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-dist…
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…
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…
Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing
Nanyang Ye, Qiao Sun, Yifei Wang +10
Analog computing using non-volatile memristors has emerged as a promising solution for energy-efficient deep learning. New materials, like perovskites-based memristors are recently…
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…