Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives
Abstract
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 view of multimodal materials data spanning chemical composition, microstructure, processing, and testing and characterisation, showing that current evidence remains concentrated in composition and idealised structure while heterogeneous, under-represented and weakly integrated modalities limit support for physical and deployment novelty. It also highlights the limitations of benchmarks based mainly on computational labels and proxy novelty criteria. Community-wide standards for data collection, modality alignment and evidence synthesis are needed to support multimodal data construction, process-aware multimodal modelling, feasibility-first generative modelling and deployment-aware benchmarking, so that generative and multimodal AI can design experimentally realisable materials with defensible scientific and practical novelty.
Cite
@article{arxiv.2607.21660,
title = {Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives},
author = {Xianyuan Liu and Charles Anjah and Benjamin E. Jolly and Jonathon F. S. Markanday and Joshua Berry and Haolin Wang and Nicola A. Morley and Robert D. J. Oliver and Alexandra J. Ramadan and Delvin Ce Zhang and Katerina A. Christofidou and Haiping Lu},
journal= {arXiv preprint arXiv:2607.21660},
year = {2026}
}