This case study highlights a collaboration between Citrine Informatics and SLAC National Accelerator Laboratory to develop an AI‑guided, closed‑loop system for the design and synthesis of catalytic nanoparticles. Starting with no historical data, the autonomous workflow combined machine learning with computer‑controlled reactors to rapidly explore process parameters. Within just 12 hours, the system achieved multiple target objectives, successfully producing nanoparticles with desired size and shape. The approach demonstrates how closed‑loop AI methodologies can dramatically accelerate materials discovery and enable reliable nanoparticle synthesis in new alloy systems, reducing trial‑and‑error and advancing catalytic materials research.

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