Congratulations, Aberdeen!

Aberdeen Dinius won Outstanding Research in Engineering and Excellence in Innovation RISE ($1000 cash) awards at RISE 2019 for her research poster titled “Transition State Theory Calculations for Hydrogen Abstractions Reactions of Biofuels”.

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DOE Grant: Exascale-enabled computational tools for complex chemical systems

Exascale Catalytic Chemistry (ECC)

Led by a team from Sandia National Laboratories, and in collaboration with Argonne National Laboratory, Pacific Northwest National Laboratory, and Brown University, our group in the Chemical Engineering department at Northeastern University is pleased to begin work on an $8M project to develop a suite of computational tools that will allow scientists and engineers to leverage the next generation exa-scale computers to build predictive models of complex chemical systems including heterogenous catalysis coupled with gas-phase reactions.

Our efforts at Northeastern will focus on developing our AutoTST software that automates transition state theory calculations of reaction kinetics, and our RMG-Cat software that is a fully automated Reaction Mechanism Generator for Heterogeneous Catalysis.

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Congratulations, Priyanka!

Priyanka successfully defended her master’s thesis, titled, “Development of RMG-Electrocat for Electrochemical Kinetic Analysis of Solid Oxide Fuel Cells.”  She will go on work as a Senior Consultant at Navigant!

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Posters at the 37th International Symposium on Combustion

Three posters were presented by Dr. Richard West at the 37th International Symposium on Combustion in Dublin, Ireland:

Poster 4P010

Poster 1P003

Poster 1P228

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Krishna attends a Full Stack Deep Learning workshop

Second year PhD student Krishna Sirumalla was one of 150 people selected from a pool of 3000 applicants to attend a Full Stack Deep Learning workshop at the University of California, Berkeley, this summer. He is using machine learning to predict properties of molecules and reactions, and will be using what he learns at the workshop to help predict the combustion reactions of halogenated hydrocarbon fire suppressants and refrigerant fluids as part of Prof. West’s NSF CAREER award.

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Boston Academic Researchers Symposium

The Boston Academic Researchers Symposium is a Chemical Engineering conference organized by Northeastern University Department of Chemical Engineering’s graduate student council, which took place today at Northeastern University.  Researches from Massachusetts Institute of Technology, Harvard University, Boston University, Tufts University, and the University of Massachusetts at Boston were present.

Nate Harms presented a poster on AutoTST, like the one he’ll be sending to Ireland next week.

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Congratulations, Rasha!

Rasha Atwi has successfully defended her master’s thesis on July, 17th 2018 titled “A Kinetic Study of the Formation of Nitrogen Heterocycles During Hydrothermal Liquefaction of Micro-algae.” She will be going on to pursue a PhD at Tuft’s University!

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NSF CAREER Grant!

In 2018 Prof. West won an NSF CAREER award for a $503,888 grant titled “CAREER: Predictive kinetic modeling of halogenated hydrocarbon combustion.”

Halogenated hydrocarbons (HHCs) are widely used as both refrigerants and fire suppressants. Driven by environmental and economic considerations, there is rapid innovation in the industry, but the next generation of HHC compounds raise fire safety concerns. Predicting the combustion behavior of these novel HHCs earlier in the design process will save much time, effort, and expense. The chemical kinetic models for describing HHC combustion are highly complex, comprising thousands of elementary reactions involving hundreds of chemical species. To effectively predict these combustion behaviors, we must automate the construction of kinetic models. This project will use a computational approach known as machine learning to help model these complex reacting systems. This breakthrough will enable us to develop an automated reaction mechanism generation tool to create detailed kinetic models for combustion of HHCs. The methodology proposed in this work are not only novel and necessary, but will be widely applicable in other aspects of automated mechanism generation. The integrated educational objective of this CAREER project is to develop a series of computational modules teaching students to solve problems throughout their chemical engineering curriculum.

The research approach is to extend and apply automated Reaction Mechanism Generator (RMG) software to create detailed kinetic models for combustion of any mix of hydrocarbons containing any combination of halogen atoms. Optimized decision-tree and novel convolutional neural network algorithms from the field of machine learning will be extended to enable the necessary restructuring of parameter estimation codes. Quantum chemistry calculations will be automated to supplement literature searches to generate the necessary training data. The model-generating tool will be validated against available experimental data from key example compounds, and used to explain the remarkable combustion behavior of these compounds. The educational program is aligned with the research, developing a series of computational modules that will be integrated into existing classes. These modules will teach students to use Python and SciPy to solve chemical engineering problems. The integration of teaching modules for scientific computing throughout the undergraduate chemical engineering curriculum will help prepare a generation of graduate engineers for a workplace in which data analysis, processing, and computation are increasingly important.

Project abstract at NSF

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CEFRC Summer School

CEFRC Summer School at Princeton University

Graduate students Emily, David, Krishna, and Nate attended the Combustion Energy Frontier Research Center’s (CEFRC) Combustion Summer School, held at Princeton University, from June 24-29, 2018.

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Welcome Chikpezili Ajulu!

We are pleased to welcome Chikpezili (Zil) Ajulu (M.S) from mechanical engineering to the CoMoChEng group!

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