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Two Carnegie Mellon University scientists looking at automated lab equipment, behind glass.

Transforming Science and Engineering to Solve Humankind’s Biggest Challenges

Complicated challenges like chronic disease, food security and climate change can’t wait for science as usual. Carnegie Mellon University is reshaping the future of science by pairing human expertise with safe and trustworthy AI, computation and automated laboratories(opens in new window) to revolutionize the way researchers work.

By connecting breakthrough technologies across the physical and virtual worlds — using automated laboratories, powerful foundation AI and physics-based models driven by high-performance computing and multimodal data resources — scientists can solve problems that were previously too complex to tackle.

“Advancing AI capabilities in science and engineering not only accelerates the pace of innovation, it makes breakthroughs possible that could have never happened before AI became a partner in human discovery and data analysis.”  —  Theresa Mayer, Vice President for Research

Automated Science Lab Changes the Pace of Scientific Discovery

Our remote-controlled, high-throughput automated science lab(opens in new window) frees up researchers to do more creative analysis and strategic thinking by automating and optimizing complex workflows that are fully traceable and reproducible.

Interconnected Scientific Instruments

24 Hours
Running Each Day

365 Days
Operating a Year

The Scientific Process Reimagined

By harnessing its strengths in responsible AI, automated technologies, machine learning and the sciences, CMU is transforming each part of the scientific process: rapidly conducting background research, identifying creative new hypotheses, accelerating experimentation and then leveraging vast amounts of multimodal data to inform the next experiment.

CMU scientists have demonstrated(opens in new window) that AI systems can autonomously plan, design and execute within minutes a chemical reaction so complex it earned its human inventors a Nobel Prize in 2010. 

Trustworthy AI, Transformative Collaboration

Three scientists huddled around a monitor, discussing AI-driven data on the screen and a physical report.

AI for Science enables trustworthy and transparent collaboration among researchers across existing and still-undiscovered disciplines, particularly those at the intersection of biology, chemistry, physics, materials, computer science and engineering.

By using data from diverse sources, multidisciplinary knowledge can be applied to a wide range of scientific and engineering problems, connecting previously disparate fields into something greater than the sum of their parts.

More about AI for Science at CMU