ABSTRACT The rapid advancement of large model technology in recent years has ushered in new opportunities for materials science. Leveraging their powerful feature learning capabilities, emergent properties, and flexible fine‐tuning mechanisms, large models are gradually being applied to all aspects of materials science research. However, we contend that the deep integration of artificial intelligence and materials science urgently requires a transition from “virtual intelligence assistance” to “embodied intelligence dominance”. Future materials discovery will be autonomously executed in closed‐loop operations within physical environments by Embodied AI Chemists. This review first introduces the blossom of large model technology and its multifaceted applications in various materials science‐related topics. Subsequently, this review specifically maps the developmental pathways of autonomous experimental platforms, underscoring the comparative advantages of large‐model‐based systems over traditional machine learning approaches. Furthermore, it discusses the potential of embodied large models in materials science and proposes potential applications by constructing diverse training datasets, enhancing embodied reasoning capabilities, establishing intelligent collaborative environments, and developing multi‐agent collaborative frameworks. Through this comprehensive analysis, we aim to pioneer new intelligent pathways for materials science research, motivating an end‐to‐end intelligent transformation from theoretical exploration to experimental realization.
Song et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: