SPACESHIP: Autonomous Mapping of Hardware-Dependent Synthesizable Space in Solution-Phase Gold Nanomaterials

Autonomous laboratories hold great promise for accelerating materials discovery but often inherit hidden limits because experimental boundaries have been predefined by human intuition or literature precedents. Such a priori constraints risk excluding feasible regions, particularly since synthesizable conditions can shift with hardware or environmental factors. We present SPACESHIP, an AI framework integrated with automated experimental hardware for adaptive exploration of chemical spaces free from literature- or expert-derived feasibility constraints. Through an AI-based prediction, robotic synthesis, real-time characterization, and model update, SPACESHIP combines probabilistic models with an Autopilot acquisition strategy that dynamically switches between models to refine synthesizable regions using both successful and failed experiments. Applied to gold nanoparticle (NP) and nanorod (NR) synthesis, this AI-robotics system achieved 90% accuracy in only 23 experiments, compared with 512 required for the ground truth. It uncovered distinct growth regimes across optical property classes and expanded synthesizable regions by factors of 8 (NPs) and 4 (NRs) beyond literature maps, adapting to hardware-specific conditions rather than relying on fixed, external constraints. By merging machine learning with autonomous experimentation, SPACESHIP addresses the long-standing reproducibility gap in science by diagnosing and adapting to system-specific synthesizable boundaries that shift across laboratories and environments, rather than assuming one universal map.