ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation

In reactive molecular dynamics (MD) simulations, such
as those
used to model combustion, filtering noisy data from reactive trajectories
is crucial for accurately constructing reaction networks and elucidating
macroscopic mechanisms. To address this challenge, we introduce a
graph algorithm-based explicit denoising approach that defines user-controlled
operations for removing oscillatory reaction patterns, including combination
and separation, isomerization, and node contraction. This algorithm
is implemented in ReaxANA, a parallel Python package designed to extract
reaction mechanisms from both heterogeneous and homogeneous reactive
MD trajectories. ReaxANA operates solely on atomic position data,
enabling its easy integration with various simulation platforms. We
demonstrate its capabilities through the analysis of the TNT (trinitrotoluene)
explosion system generated by using molecular dynamics simulations
with the ReaxFF force field. ReaxANA effectively distinguishes structural
isomers, facilitating a comprehensive examination of reaction networks.
Our findings reveal that the primary decomposition pathway of TNT
involves pyrolysis of the ortho nitro group (-NO2), followed
by further decomposition that leads to a five-membered ring compound.
ReaxANA is an open-source software and packaged in a Docker container
for cross-platform compatibility, providing insights and advanced
analytical capabilities.