Detailed chemical kinetic modeling continues to gain interest as an approach to study reactive chemical systems, ranging in application from combustion and pyrolysis of fuels to degradation of active pharmaceutical ingredients. This growth can be attributed to a combination of demand for studying increasingly complex chemistries and supply of computational power and quantum chemistry capabilities. By taking advantage of these computational resources, automatic mechanism generation tools are able to systematically enumerate and evaluate potential chemical pathways, reducing the chance of human error. This is largely a data-driven task, requiring good estimation algorithms for thermochemical and rate parameters, which in turn rely on accurate training data from experiments or quantum chemistry calculations.