John Kitchin
Professor, Chemical Engineering
Courtesy Appointment, Materials Science and Engineering
John Kitchin studies catalysis on metals and metal oxides using density functional theory. He develops software for modeling materials, solving engineering problems, and writing scientific documents. He also studies new materials for CO2 capture applications.
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.
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
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.
Chemical Engineering
Kitchin to receive the Award for Innovation in Chemical Engineering Education
ChemE’s John Kitchin will receive the Award for Innovation in Chemical Engineering Education from the American Institute of Chemical Engineers.
Chemical Engineering
Kitchin to receive Award for Innovation in Chemical Engineering Education
AIChE recognizes John Kitchin for his impact on modern chemical engineering pedagogy and computational research.
Chemical Engineering
Making sense of too much data
With hundreds of research papers published each day, synthesizing all of the available information for literature reviews has become increasingly difficult. Now, professors and librarians at Carnegie Mellon University are teaming up to find and teach unique techniques to uncover pertinent information for academic studies.
Chemical Engineering
Kitchin develops STEM-focused Python course
Carnegie Mellon Chemical Engineering Professor, John Kitchin, is carving out a path for scientists and engineers to learn the computer programming language, Python. His new nine-booklet series combines over 20 years of knowledge with an easy-to-follow, step-by-step approach.
Chemical Engineering
Researchers develop package to accelerate geometry optimization in molecular simulation
John Kitchin is speeding up molecular simulation by providing a neural network-based active learning method that accelerates geometric optimization.
CMU Engineering
Move to remote research invites innovation
While much of our lives can now function remotely, the transition to online poses unique challenges for academia—particularly for research universities like Carnegie Mellon.