John Kitchin
John E. Swearingen Professor, Chemical Engineering
Courtesy Appointment, Materials Science and Engineering
John Kitchin works at the intersection of machine learning, data science, and scientific programming with science and engineering. He develops software for modeling materials, solving engineering problems, and writing scientific documents. He uses these tools to model catalysts with applications in energy, to solve inverse problems in engineering, and to find new approaches in developing surrogate models for engineering systems.
Kitchin completed his B.S. in chemistry at North Carolina State University. He completed an M.S. in materials science and a Ph.D. in chemical engineering at the University of Delaware in 2004 under the advisement of Dr. Jingguang Chen and Dr. Mark Barteau.
He received an Alexander von Humboldt postdoctoral fellowship and lived in Berlin, Germany for 1½ years studying alloy segregation with Karsten Reuter and Matthias Scheffler in the Theory Department at the Fritz Haber Institut. Kitchin began a tenure-track faculty position in the Chemical Engineering Department at Carnegie Mellon in January 2006. He was awarded a DOE Early Career award in 2010. He received a Presidential Early Career Award for Scientists and Engineers in 2011. He completed a sabbatical in the Accelerated Science group at Google learning to apply machine learning to scientific and engineering problems in 2018. In 2023, he was the recipient of the AIChE Award for Innovation in Chemical Engineering Education.
Using Machine Learning to Improve Molecular Simulations
Education
2004 Ph.D., Chemical Engineering, University of Delaware
2002 MS, Materials Science and Engineering, University of Delaware
1996 BS, Chemistry, North Carolina State University
Media mentions
Chemical Engineering
Machine learning model predicts magnetic properties of materials
With state-of-the-art prediction accuracy and data efficiency, a new model makes it more feasible to design magnetic materials and understand the effects of magnetism in catalysis.
TechVibe Pittsburgh
Kitchin discusses Carnegie Mellon joining MDS-Rely
ChemE/MSE’s John Kitchin was featured on the TechVibe Pittsburgh podcast to discuss Carnegie Mellon joining the MDS-Rely research cooperative.
Chemical Engineering
Carnegie Mellon University announced as third MDS-Rely site
Led by John Kitchin, the CMU team brings expertise at the intersection of data science and artificial intelligence to an industry-university collaboration that is advancing materials development.
Chemical Engineering
Kitchin awarded Swearingen Professorship
The College of Engineering has awarded the John E. Swearingen Professorship of Chemical Engineering to John Kitchin.
Chemical Engineering
New tool brings vector search to the scientific literature
John Kitchin created litdb, a Python package that helps researchers curate and use their own literature database.
Chemical Engineering
Instrument teaches skills for automated experimentation
John Kitchin built Claude-Light to teach students and researchers how to control a remote instrument and how to analyze the data that comes from it.
Chemical Engineering
Scott Institute strengthens partnership with NETL
After a campus visit from the director of the National Energy Technology Lab (NETL), chemical engineering faculty reflect on the history and evolution of research collaborations.
CMU Engineering
Interdisciplinary team partners to develop advanced alloys
Materials science and engineering and chemical engineering faculty will collaborate on projects supported by the Naval Nuclear Laboratory to create additively manufactured structural alloys that can sustain extreme environments.
Chemical Engineering
Machine learning framework finds catalysts for lower-cost hydrogen production
Researchers developed an ML framework to identify a more stable catalyst for water splitting.
Chemical Engineering
Waveform design improves catalyst performance
With a faster, reliable, and open-source mathematical framework for simulating and optimizing dynamic catalysis, researchers design the ideal waves for high catalyst activity.
Chemical Engineering
Alum spotlight: Dennis Loevlie
In John Kitchin’s research group and now at an autonomous flight startup, Dennis Loevlie (‘21) applies concepts from both chemical engineering and computer science.
Chemical Engineering
Data science and machine learning approaches to catalysis
In a keynote at the AIChE annual meeting, John Kitchin illustrated what is possible when we think broadly about data science and machine learning.