  In many scientific fields, there is an interest in understanding the way in
which complex chemical networks evolve. The chemical networks which researchers
focus upon, have become increasingly complex and this has motivated the
development of automated methods for exploring chemical reactivity or
conformational change in a black box manner, harnessing modern computing
resources to automate mechanism discovery. In this work we present a new
approach to automated mechanism generation implemented which couples molecular
dynamics and statistical rate theory to automatically find kinetically
important reactions and then solve the time evolution of the species in the
evolving network. Key to this ChemDyME approach is the novel concept of kinetic
convergence whereby the search for new reactions is constrained to those
species which are kinetically favorable at the conditions of interest. We
demonstrate the capability of the new approach for two systems, a well-studied
combustion system, and a multiple oxygen addition system relevant to
atmospheric aerosol formation.
