Publication date: 22nd July 2026
AI-driven autonomous laboratories have accelerated materials discovery by adapting conventional automated equipment like liquid handlers, robotic arms, spin coaters, to replace human-executed workflows. While these systems have successfully automated traditional experimentation, they remain constrained by a fundamental design limitation: chemical processes built around a human throughput model, where experiments run sequentially rather than in parallel.
We argue that this model of laboratory automation represents a ceiling, not just a bottleneck and we need to reimagine chemistry and chemical labs to build AI-native labs, which does not limit AI, but make use of its full potential to accelerate materials innovation. For. e.g. even though a batch Bayesian optimization proposes many experiments at once, they are still physically executed one by one due to laboratory constraints, with results fed back only after all the experiments have been executed. Unlike human chemists, AI can plan, execute and analyze parallel experimental streams, yet current automated laboratories are not utilizing this capacity of AI to its full potential.
We challenged ourselves to address this challenge and build an AI-native physical laboratory architected from the ground up for AI execution, not human adaptation. Rather than automating human processes, HELIOS reimagines chemical synthesis as a massively parallel, AI-directed operation capable of running one million material experiments per minute. As a proof of concept, we apply HELIOS to the exploration of the vast perovskite chemical space for energy applications, demonstrating that AI-native lab design can unlock a scale of experimentation previously inaccessible to materials science.
