Congratulations, Krishna, for winning a CRE travel award!

Krishna Sirumalla won the Catalysis and Reaction Engineering Division travel award to attend the 2019 AICHE annual meeting in Orlando, Fl, where he will present three talks: Message Passing Attention Networks for Reaction Rate Estimation, Autonomous Systems for Experimental and Data-Driven Modeling of Combustion Kinetics, and An Automated Management Framework for High Fidelity Quantum Chemistry Calculations.

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NSF Funding for Cantera Development

The CoMoChEng group at Northeastern University is part of a collaborative effort to further develop and support the successful and popular open-source modeling software Cantera, a suite of tools for problems involving chemical kinetics, thermodynamics, and transport processes.

Together, we recently secured $2.5m funding from the NSF Office of Advanced Cyberinfrastructure (OAC) under the Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program. The project, titled “Frameworks: Collaborative Research: Extensible and Community-Driven Thermodynamics, Transport, and Chemical Kinetics Modeling with Cantera: Expanding to Diverse Scientific Domains“, will develop the Cantera software platform in service of three objectives: (i) extend Cantera’s scientific capabilities to support the development of transformative technologies; (ii) expand Cantera’s user base in fields including electrochemistry, heterogeneous catalysis, and atmospheric chemistry; and (iii) broaden participation in the software’s development and management to improve Cantera’s sustainability and usability.

Cantera logo

Learn more about Cantera at

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RMG-Cat at North American Catalysis Society Meeting

Emily presented the latest advancements in RMG-Cat at 26th North American Catalysis Society Meeting which took place in Chicago from 23-28 June 2019.

  1. E. Mazeau, K. Blondal, C. F. Goldsmith, and R. H. West. Automated Construction of Microkinetic Models with RMG-Cat for Mapping the Degree of Rate Control. 26th North American Catalysis Society Meeting (NAM26). Chicago, IL. 23 – 28 June 2019

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AutoTST at International Numerical Conference on Combustion

Prof. West and Nate presented AutoTST-2.0 at International Numerical Conference on Combustion which took place in Aachen, Germany from 6 – 8 May 2019

  1. R. H. West and N. Harms. AutoTST: automated transition state theory calculations for high- throughput calculation of chemical kinetics. 17th International Conference on Numerical Combustion. Aachen, Germany. 6 – 8 May 2019.

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Congratulations, Aberdeen, RISE Award Winner!

Aberdeen Dinius, an undergraduate researcher in the CoMoChEng lab, won both the Outstanding Student Research award in the Engineering and Technology (Undergraduate) category and the overall RISE Excellence in Innovation award (with $1,000 cash) at Northeastern’s Research Innovation and Scholarship Expo (RISE) 2019 for her research poster titled “Transition State Theory Calculations for Hydrogen Abstractions Reactions of Biofuels”.

Photo of Aberdeen standing in front of her poster.
Aberdeen presenting her poster at the RISE expo
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Presentations at US national combustion meeting

The 11th national combustion meeting took place in Pasadena, CA from 24-27th March 2019, where Prof. West and David presented three talks.

  1. K. E. Niemeyer, R. L. Speth, B. W. Weber, and R. H. West. A review of evidence-based best practices for developing research software in combustion. 11th US National Combustion Meeting. Pasadena, California. 24 – 27 March 2019. (Zenodo)
  2. R. H. West and C. F. Goldsmith. The effects of roaming radical reactions on global combustion properties of transportation fuels. 11th US National Combustion Meeting. Pasadena, California. 24 – 27 March 2019.
  3. D. Farina, S. K. Sirumalla, D. Sotir, and R. H. West High Fidelity Thermochemistry for Kinetic Modeling of Methyl Chloride Combustion. 11th US National Combustion Meeting. Pasadena, California. 24 – 27 March 2019.

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Posters at Research, Innovation and Scholarship Expo (RISE)

The 2nd RISE Expo took place on 4th March 2019 where undergraduate students Carl, Aberdeen, Ben, and Anthony presented three posters.

1. C. Underkoffler, N. Harms, and R. H. West. Including 1-D hindered rotors of molecular geometry in automated transition state theory calculations. RISE: Research, Innovation, and Scholarship Expo. Northeastern University, Boston, MA. 4th April 2019.
2. A. Dinius‡, N. Harms†, and R. H. West. Transition state theory calculations for hydrogen abstraction reactions of biofuels. RISE: Research, Innovation, and Scholarship Expo. Northeastern University, Boston, MA. 4th April 2019. (Winner of overall “Innovation” and “Outstanding Student Research: Engineering and Technology” awards)
3. A. Stohr, B. Hoare, N. Harms, R. H. West, Automatically analyzing the accuracy of combustion mechanisms through jet-stirred reactor simulations. RISE: Research, Innovation, and Scholarship Expo. Northeastern University, Boston, MA. 4th April 2019.

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Presentations at 2018 annual AIChE national meeting at Pittsburgh

The Annual AIChE national meeting took place at David L. Lawrence Center, Pittsburgh from October 28 – November 2, where Nate, Krishna, and Emily presented three talks.

  1. N. D. Harms, S. K. Sirumalla†, and R. H. West. Assessing Discrepancies in Kinetic Parameters and Improving Combustion Models through Metaheuristic Optimization. AIChE Annual Meeting. Pittsburgh, PA. 28 October – 2 November 2018.
  2. E. Mazeau, D. Farina, R. H. West, K. Blondal, and C. F. Goldsmith. Mapping the Degree of Rate Control Using Automated Construction of Microkinetic Models with RMG-Cat. AIChE Annual Meeting. Pittsburgh, PA. 28 October – 2 November 2018.
  3. S. K. Sirumalla, N. D. Harms, and R. H. West. Transition State Geometry Prediction Using Neural Embeddings of Transition State Graphs. AIChE Annual Meeting. Pittsburgh, PA. 28 October – 2 November 2018.

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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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NSF CDS&E Grant for “AutoScience”

In September 2018, we began work on an exciting new project in collaboration with Mike Burke’s group at Columbia University, that we call the “AutoScience” project. The goal is to couple automated calculations and automated experiments together using automatically generated models that are automatically analyzed. Then we can all retire!

The collaborative 3-year project is supported by Computational and Data-Enabled Science and Engineering program (CDS&E) at the NSF, and the project abstract at the NSF is like this:

To meet pressing societal needs for more cost-effective and sustainable energy, future combustion engines need to be more fuel-efficient, produce less emissions, and operate on a variety of fuels, including alternative fuels. Engineers often use computer models of fuel combustion chemistry to design engines with improved performance and determine the suitability of a certain fuel in an engine. In producing combustion models for engineers to use, scientists usually start by creating a trial model, then generate computational and experimental data to test the model, and improve and validate the model against the data. The latter two tasks are often repeated until the resulting model is sufficiently accurate for reliable use. Present techniques for developing reliable, validated models for transportation-relevant fuels typically involve combining the efforts of multiple research groups, taking multiple years or even decades to obtain enough data. The present approach for developing fuel combustion chemistry models is insufficient to address pressing energy needs in a timely and effective manner, particularly as many potential modern fuels have not been well characterized. This project will create and test the performance of a new autonomous system that creates trial models, generates data, and makes model improvements to rapidly converge on a reliable, validated, fuel chemistry model. Successful implementation of the novel autonomous system will provide an advanced model development tool for combustion kinetics and an accelerated means of understanding the oxidation behavior of the many alternative fuels, which governs their viability. Finally, this project will engage undergraduate and graduate students in research and create novel teaching modules for data science applied to combustion kinetics. The modules will enhance proficiency of younger generations of students in the scripting and data science tools necessary to ensuring a competitive STEM program in the U.S.

The technical objective of this project is to create an autonomous system for studying fuel oxidation chemistry and evaluate its performance relative to current time-intensive approaches. This autonomous system will use a multi-physics uncertainty quantification framework, MultiScale Informatics, to integrate an automated kinetic model construction platform, Reaction Mechanism Generator, an adaptable automated High-Throughput Jet Stirred Reactor experiment, and an algorithm for performing automated quantum chemistry, statistical thermodynamics, and transition state theory calculations (AutoTST). By linking the uncertainties both in experimental observables in the Jet Stirred Reactor and in Quantities of Interest, such as onset of ignition in an engine, to physically meaningful parameters in the kinetic model, such as barrier heights of a reaction, calculations and experiments can be optimally designed to improve the model’s accuracy for predicting Quantities of Interest. This project seeks to (1) create the autonomous platform, (2) use it to generate a model for n-heptane, for which previous data and models are relatively mature, to assess its performance, and (3) apply it to diisobutylene, a promising biofuel recently identified in the DOE’s Co-Optima program. This project will create a new data-driven approach for combustion research at an accelerated pace, contribute to scientific understanding for n-heptane and diisobutylene, and, more broadly, contribute to understanding of autonomous science.

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