4 citations · 7 across the 6 of their papers we have counts for
6 papers
Multi-Source Domain Transfer Learning for Accurate Property Prediction in Two-Dimensional Materials
Huiyang Zhang, Xinyu Chen, Qionghua Zhou +1
Machine learning has revolutionized materials discovery, but data scarcity remains a critical bottleneck for complex functional properties. As emerging systems, two-dimensional (2D…
Physics-grounded generative design of inherently stable, novel and controllable crystal structures
Zhilong Song, Qionghua Zhou, Chongyi Ling +3
Generative inverse design is reshaping the discovery of functional crystalline materials. Yet current generative models face challenges in simultaneously achieving stability, novel…
LLM-Feynman: Leveraging Large Language Models for Universal Scientific Formula and Theory Discovery
Zhilong Song, Qionghua Zhou, Chunjin Ren +3
Distilling underlying principles from data has historically driven scientific breakthroughs. However, conventional data-driven machine learning often produces complex models that l…
Is Large Language Model All You Need to Predict the Synthesizability and Precursors of Crystal Structures?
Zhilong Song, Shuaihua Lu, Minggang Ju +2
Accessing the synthesizability of crystal structures is pivotal for advancing the practical application of theoretical material structures designed by machine learning or high-thro…
T2MAT (text-to-materials): A universal agent for generating material structures with goal properties from a single sentence
Zhilong Song, Shuaihua Lu, Qionghua Zhou +1
Artificial Intelligence-Generated Content (AIGC)-content autonomously produced by AI systems without human intervention-has significantly boosted efficiency across various fields.…
Inverse Design of Promising Alloys for Electrocatalytic CO Reduction via Generative Graph Neural Networks Combined with Bird Swarm Algorithm
Zhilong Song, Linfeng Fan, Shuaihua Lu +3
Directly generating material structures with optimal properties is a long-standing goal in material design. One of the fundamental challenges lies in how to overcome the limitation…