Autonomous laboratories for sustainable nanomaterials discovery

Autonomous nanomaterials discovery is rapidly transforming conventional trial-and-error experimentation into intelligent closed-loop scientific ecosystems that integrate artificial intelligence (AI), robotics-assisted experimentation, autonomous characterization, and cyber–physical laboratory infrastructures. Unlike previous reviews that primarily focus on individual enabling technologies, this critical review presents a systems-level synthesis of autonomous nanomaterials discovery by critically evaluating AI-guided optimization, robotics-assisted synthesis, multimodal characterization, digital twins, foundation models, and sustainability-aware autonomous experimentation within a unified scientific framework. Particular emphasis is placed on the comparative capabilities of machine learning, graph neural networks, generative AI, Bayesian optimization, active learning, and reinforcement learning for adaptive materials discovery, together with their practical limitations in autonomous laboratory environments. Representative applications involving perovskite materials, catalytic nanomaterials, battery materials, and autonomous nanoparticle synthesis illustrate the expanding role of intelligent experimentation platforms in accelerating materials innovation. The review further examines key challenges associated with data quality, uncertainty propagation, interpretability, interoperability, cyber–physical reliability, computational sustainability, and responsible governance that continue to constrain large-scale deployment. Finally, emerging distributed autonomous laboratories, collaborative human–AI scientific workflows, and sustainability-aware multi-objective optimization are identified as key strategic directions for developing trustworthy, resilient, and environmentally responsible autonomous scientific ecosystems.