This paper shows how AI can be integrated with automated blocc chemistry to scale molecular discovery beyond a single lab. The authors built a cloud-based platform that coordinates closed-loop discovery cycles across multiple sites asynchronously around the world. In this platform, AI proposes new candidates, distributed robotic systems synthesize and characterize them where capacity and expertise exist, and data flows back to update a machine learning model in real time.
Applied to organic solid-state laser gain materials, the system rapidly navigated a vast chemical space and delivered many newly discovered, top-performing laser emitters. Because experiments were run in parallel across the world, the closed-loop concept accelerated discovery not by working more efficiently in one place, but by orchestrating many capable nodes at once.
In addition to identifying a top-in-class laser emitter, a key advance was the workflow: a practical blueprint for “globalized” AI-enabled molecular discovery harnessing the power of blocc chemistry. It demonstrates that future materials R&D can be faster, more resilient, and more accessible when AI connects specialized labs into one shared, self-improving experimental network — essentially democratizing high-end molecular discovery pipelines.
