Predictive detailed kinetic modeling of complex reacting systems.
The common thread to our diverse research projects is automating the construction of detailed microkinetic models – models comprising dozens, hundreds, thousands, or even hundreds of thousands, of intermediate chemical species and elementary reactions. Most of our projects in some way involve the open-source Reaction Mechanism Generator software “RMG”. We also run Density Functional Theory (DFT) calculations, employ Machine Learning (ML), perform Uncertainty Quantification (UQ), and Global Sensitivity Analysis (GSA). We apply these tools to challenges in fields such as combustion, PFAS remediation, heterogeneous catalysis, and electrochemistry.
- Halogenated Species in RMG: Adding PFAS Chemistry, Exploring Halocarbon Blends, and Reducing Halocarbon Models with ML
Nora Khalil’s research focuses on improving the modeling of halogenated chemical systems—particularly PFAS and halocarbon refrigerants/suppressants—using computational tools and machine learning. She is enhancing the Reaction Mechanism Generator (RMG) to automatically… - Accelerating the investigation of cleaner fuels with automated kinetic model improvement
Detailed kinetic models help us investigate alternative fuels for the next generation of cleaner combustion devices. While automated mechanism generators like Reaction Mechanism Generator (RMG) can build these models, the first… - Electrochemical Reduction of CO2
Using green electricity to turn CO2 into renewable fuels and chemicals….