  The high computational cost of evaluating atomic interactions recently
motivated the development of computationally inexpensive kinetic models, which
can be parametrized from MD simulations and accelerate the prediction of the
chemical evolution by up to four order of magnitude. Such kinetic models
utilize molecular descriptions of reactions and have been constrained to only
reproduce molecules previously observed in MD simulations. Therefore, these
descriptions fail to predict the reactivity of unobserved molecules, for
example in the case of large molecules or solids. Here we propose a new
approach for the extraction of reaction mechanisms and reaction rates from MD
simulations, namely the use of atomic-level features. Using the complex
chemical network of hydrocarbon pyrolysis as example, it is demonstrated that
kinetic models built using atomic features are able to explore chemical
reaction pathways never observed in the MD simulations used to parametrize
them, a critical feature to describe rare events. Atomic-level features are
shown to construct reaction mechanisms and estimate reaction rates of unknown
molecular species from elementary atomic events. Through comparisons of the
model ability to extrapolate to longer simulation timescales and different
chemical compositions than the ones used for parameterization, it is
demonstrated that kinetic models employing atomic features retain the same
level of accuracy and transferability as the use of features based on molecular
species, while being more compact and parametrized with less data. We also find
that atomic features can better describe the formation of large molecules
enabling the simultaneous description of small molecules and condensed phases.
