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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Presentations at the 256th American Chemical Society National Meeting

The 256th American Chemical Society (ACS) National Meeting took place in Boston from 19 – 23 August 2018, where Nate, Krishna, and Richard presented six talks and a poster, including work by Emily and Rasha.

  1. N. D. Harms and R. H. West. Implementing genetic algorithms and evolutionary strategies in conformer analysis. 256th ACS National Meeting. Boston, MA. 19 – 23 August, 2018.
  2. N. D. Harms and R. H. West. Automatically finding and fixing mistakes in detailed kinetic models of combustion. 256th ACS National Meeting. Boston, MA. 19 – 23 August, 2018.
  3. S. K. Sirumalla and R. H. West. Unsupervised machine learning for data-driven representations of reactions. 256th ACS National Meeting. Boston, MA. 19 – 23 August, 2018.
  4. R. Atwi, M. T. Timko, and R. H. West. Investigating reaction pathways for the formation of nitrogen heterocycles during hydrothermal liquefaction of microalgae. 256th ACS National Meeting. Boston, MA. 19 – 23 August, 2018.
  5. N. D. Harms, S. K. Sirumalla, and R. H. West. Determining kinetic parameters of hydrogen ab- straction from novel biofuel candidates through automated transition state theory calculations. 256th ACS National Meeting. Boston, MA. 19 – 23 August, 2018.
  6. R. H. West, E. J. Mazeau, and C. F. Goldsmith. Automatic microkinetic mechanism generation for heterogeneous catalysis. 256th ACS National Meeting. Boston, MA. 19 – 23 August, 2018.
  7. N. D. Harms, S. K. Sirumalla, and R. H. West. Determining kinetic parameters of hydrogen abstraction from novel biofuel candidates through automated transition state theory calculations. 256th ACS National Meeting. Boston, MA. 19 – 23 August, 2018. POSTER

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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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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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