Boston Lab Lets AI Run Real Science Experiments 24/7

Scientists in Boston just flipped on a lab where artificial intelligence designs experiments, runs them physically, and learns from the results without constant human supervision.

Atinary launched its first self-driving laboratory this week in Massachusetts, housing two autonomous platforms that work around the clock. These "Scientific Discovery Factories" run complete experiment cycles on their own, testing chemicals and materials while machine learning algorithms decide what to try next based on each result.

The breakthrough isn't just software crunching numbers. These systems physically mix chemicals, run tests, analyze what happened, and immediately use that knowledge to design better experiments. It's like having a tireless scientist who never forgets a lesson and gets smarter with every attempt.

"Self-driving labs are about bringing AI into contact with reality," said Hermann Tribukait, Atinary's CEO and co-founder. The company aims to solve a massive bottleneck in scientific research: most experiments still happen through slow, manual trial and error that can stretch discoveries across decades.

The lab will start with small-molecule synthesis and catalysis, directly supporting pharmaceutical companies hunting for new drugs. Instead of researchers spending months on repetitive tests, the AI handles the grunt work while human scientists focus on creative problem-solving and strategic decisions.

Atinary's team includes MIT Professor Stephen Buchwald, ranked as the world's most cited chemist for a decade. Their approach deliberately keeps humans in the loop, using AI to amplify rather than replace scientific imagination and judgment.

The Ripple Effect

The implications stretch far beyond one lab in Boston. Faster, more reliable experiments could accelerate treatments for diseases, create better materials for clean energy, and solve chemistry problems that have stumped researchers for generations. The platform is designed to scale across chemical and materials sciences, potentially transforming how discovery happens in dozens of fields.

Higher reproducibility means other scientists can trust and build on these results more confidently. Better data quality leads to breakthroughs that actually work when scaled up. And continuous learning means the system gets more efficient with every experiment, compounding progress over time.

By running experiments at machine speed while maintaining human oversight, this model could democratize access to cutting-edge R&D infrastructure that typically only massive corporations can afford.

The Boston facility represents a shift from AI as a tool that observes to AI as a partner that does, bridging the gap between computational predictions and real-world validation at a pace science has never seen before.