Predictive Automated Combustion Chemistry: Massively Parallel High-Accuracy Thermochemistry

High fidelity mechanisms are crucial for improving the efficiency of combustion devices. We are developing a fuel chemistry code that automates reaction mechanism generation (RMG) with accurate thermochemical kinetics. Accurate computation of thermochemical parameters requires careful treatment of electronic and nuclear degrees of freedom. We couple accurate determinations of the lowest energy torsional conformer with extrapolation of electronic energies to the complete-basis-set, complete-correlation limit, and detailed examination of anharmonic vibrational effects in predicting the partition functions. To do so, we implemented a Python code, QTC, which automates all aspects of the workflow providing a unified interface for quantum chemistry packages and other codes developed by our team for torsional scans and partition function calculations. Parallelization through Swift scripts allows for very high strong scaling efficiency on supercomputers. Our approach is illustrated by generating a list of important species for butane combustion with RMG followed by large-scale automated benchmark thermochemistry calculations with QTC.