5 papers · 1 filter
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…
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.…
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…
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…