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.