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.
Carly successfully defended her Ph.D. thesis on "MultiScale Data-Driven Modeling of Foundational Combustion Reaction Systems"
Congrats, Dr. LaGrotta!
C.E. LaGrotta, Q. Meng, L. Lei, M.C. Barbet, Z. Hong, M.P. Burke, “Resolving Discrepancies Between State-of-the-Art Theory and Experiment for HO2 + HO2 via Multiscale Informatics,” Journal of Physical Chemistry A 127 (2023) 799–816.
R.E. Cornell, M.C. Barbet, J. Lee, M.P. Burke, “NH3 Oxidation by NO2 in a Jet-Stirred Reactor: The Effect of Significant Uncertainties in H2NO Kinetics,” Applications in Energy and Combustion Science 12 (2022) 100095.