  The high computational cost of evaluating atomic interactions recently
motivated the development of computationally inexpensive kinetic models, which
can be parametrized from MD simulations of complex chemistry of thousands of
species or other processes and accelerate the prediction of the chemical
evolution by up to four order of magnitude. Such models go beyond the commonly
employed potential energy surface fitting methods in that they are aimed purely
at describing kinetic effects. So far, 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. 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.
