About Us

Understanding and being able to predict the outcomes of complex reacting systems would be invaluable to varied scientific and engineering disciplines, ranging from enabling future energy, defense, and chemical processing technologies to understanding the Earth’s climate and the origins of life.  Yet, unraveling and making predictions of complex reacting systems are notoriously challenging tasks.  Given the complex and multiscale nature of many systems, a common theme is that physics and data from a single scale are rarely sufficient to enable definitive explanations and truly predictive models.

Our group aims to achieve multiscale understanding and enable data-driven predictive modeling of complex reacting systems in diverse environments by combining physics and data across multiple scales.  To achieve this goal, we use an interdisciplinary approach that takes advantage of ab initio theoretical chemistry, multiscale modeling, and data science.  We have found that (1) models created with this approach can truly predict both molecular and macroscopic behavior over an expansive range of thermodynamic conditions and (2) the approach aids in resolving outstanding scientific questions and revealing new ones.

Recent Publication

On the Role of HNNO in NOx Formation

Qinghui Meng
Lei Lei
Joe Lee
Michael P. Burke


June 14, 2022

Dr. Rodger Cornell

Rodger successfully defended his Ph.D. thesis on "Kinetic Experiments and Data-Driven Modeling for Energetic Material Combustion"

Congrats, Dr. Cornell!

Columbia Affiliations
Mechanical Engineering
Chemical Engineering
Data Science Institute