Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives
Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery. However, novelty in materials discovery encompasses chemical plausibility, structural distinctiveness, property relevance and experimental realisability, making AI-driven novelty claims difficult to substantiate. We introduce a materials property hierarchy, from intrinsic, composition-determined properties to extrinsic, processing-dependent performance, to clarify deployment constraints and distinguish structural, physical and deployment novelty. This framework motivates an evidence-based vi...
| Authors | Xianyuan Liu, Charles Anjah, Benjamin E. Jolly et al. |
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| Primary category | cond-mat.mtrl-sci |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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