Heather J. Kulik

Heather J. Kulik is the Lammot du Pont (1901) Professor of Chemical Engineering and Professor of Chemistry at MIT. She leads the Kulik Group, focusing on computational chemistry, materials science, and machine learning for catalysis and materials discovery. Her affiliations include MIT's Department of Chemical Engineering, the Chemistry Department, and interdisciplinary centers like the Center for Enhanced Nanofluidic Transport (CENT) EFRC and the NSF Center for the Chemistry of Molecularly Optimized Networks (MONET).

Education:

• B.E. in Chemical Engineering, Cooper Union (2004)
• Ph.D. in Materials Science and Engineering, MIT (2009)
• Postdoctoral research at Lawrence Livermore National Laboratory (2010) and Stanford University (2010–2013)

Research Interests: Her work spans multi-scale modeling, electronic structure calculations, and machine learning for designing new materials (e.g., metal-organic frameworks, enzymes, organometallics). Key areas include computational catalysis, enzyme mechanism elucidation, and autonomous method selection for simulations. She emphasizes predictive accuracy in open-shell transition metal chemistry.

Articles Trends: Recent work highlights machine learning-driven discovery of catalysts (e.g., methane-to-methanol), stability analysis of metal-organic frameworks, and quantum mechanical insights into enzymatic reactions. Publications emphasize applications in energy storage, environmental remediation, and biochemical processes.

Awards:

• Burroughs Wellcome Fund Career Award (2012–2017)
• Sloan Fellowship (2021)
• AAAS Marion Milligan Mason Award (2019–2020)

Advising & Grants: Advises students on computational and experimental projects, leveraging funding from agencies like NSF, DARPA, and the Department of Energy. Recent grants support machine learning integration in catalysis and materials informatics.

Labs & Teams: Collaborates with experimental groups in biochemistry, materials science, and engineering. Her lab develops tools like the molSimplify toolkit and the CoRE MOF DB for data-driven discovery.