| Abstract Scope |
Automatic and autonomous equipment plays a crucial role in advancing self-driven laboratory (SDL) systems for accelerated materials discovery. In AI-driven research workflows, high-throughput and reliable data acquisition are essential for efficient closed-loop optimization. We present an integrated experimental platform enabling precise dosing of multi-source liquids and solids, uniform mixing, densifying pellet pressing, and single-phase formation via solid-state reaction melting and sintering. This system is tailored for ceramic, metallic, and renewable energy materials, supporting applications such as solid-state electrolytes, high-entropy alloys, catalysis, and batteries. The platform combines programmable hardware with adaptive AI algorithms to enable reproducible, high-throughput synthesis and characterization cycles. Real-time data feedback continuously refines AI predictions, guiding subsequent experiments and accelerating the discovery pipeline. This automated framework significantly reduces the trial-and-error nature of traditional approaches, offering a scalable solution for rapid innovation in energy materials research. |