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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 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

Future of the Engineering and Science of Automation in Bakery Square

Carnegie Mellon is prototyping the future of autonomous laboratories, sometimes known as “self-driving laboratories,” cloud labs, or automated laboratories. In this space, researchers control and access instruments remotely to perform research in chemistry, materials science, mesoscale energy synthesis, biology, biomedical engineering, tissue culture and more.

Located in Bakery Square, the prototype facility has an extensive collection of off-the-shelf laboratory instruments and student and CMU-spinout laboratory instruments. The facility extends the research mission of CMU’s students and collaborators by giving them the scale and access to important and cutting edge research problems.

For example, one of the new instruments addresses a critical problem in growing cells in the lab at scale for very heterogenous types of cells, all of which grow at different rates and require different and sometimes very delicate treatment, in a table-top form factor that is safe and easy to clean.

The lab will allow scientists and engineers to do more creative analysis and strategic thinking by automating and optimizing complex workflows that are fully traceable and reproducible.

Instrumentation 
General laboratory automation (liquid handling robotics), chemistry analytical instrumentation (high performance liquid chromatography, mass spectrometers, structural characterization (x-ray crystallography, nuclear magnetic resonance spectrometers, biological instrumentation (flow cytometers, high-throughput sequencing)

CMU Community
Members of the CMU community can request training to use the facility.  Important links:

Contact Us 
Armaghan (Rumi) Naik(opens in new window), Executive Director, Future of the Engineering and Science of Automation Initiative

200+
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

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