MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems. Generative large language models offer a fundamentally different paradigm, in which structural representation, quantitative prediction, and structure-activity reasoning can be unified within one model, but the materials community has yet to see this paradigm realized at a level competitive with established narrow specialists. Here we ...
| Authors | Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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