Recent progress in theoretical methodologies and computational capabilities has propelled the transformation of chemical kinetics computations from largely empirical to highly predictive. As a result, the fidelity of combustion modeling can dramatically improve. We are building a large-scale, high-level, and automated combustion chemistry modeling software package to facilitate this advancement. We will discuss the code’s ability to accurately predict the temperature and pressure dependence of the thousands of gas phase reactions in a combustion mechanism – which it achieves by providing a unified interface for mechanism generation, electronic structure theory, transition state theory, classical trajectory simulations, the master equation, and uncertainty analysis tools through a series of drivers. The Quantum Thermochemistry (QTC) python driver automates and parallelizes the process of generating thermochemical data. Using chemical names as input, QTC carries out a careful treatment of the electronic and nuclear degrees of freedom for each species. After finding a preliminary geometry via classical force fields followed by Monte Carlo sampling, QTC performs composite ab initio electronic structure calculations coupled with high-level treatments of anharmonicity and nonlocal motions, forms partition functions, and ultimately generates thermochemical data represented as NASA polynomials. A second driver (Automechanic) adds kinetics for abstraction, addition, and hydrogen migration reactions. It calls our rate constant driver (RCDriver), and its interface to EStokTP, to automate the transition state search for each reaction, generate thermochemical data, and use transition state theory and master equations to produce rate information. This novel package makes highly reliable thermochemical and kinetic data readily attainable to the combustion community.

Automating high-accuracy thermochemistry and kinetics for combustion

2019 Spring National Meeting

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