Smart Reaction Templating: A Graph-Based Method for Automated Molecular Dynamics Input Generation

Accurately modeling
chemical reactions in molecular dynamics simulations
requires detailed pre- and postreaction templates, often created through
labor-intensive manual workflows. This work introduces a Python-based
algorithm that automates the generation of reaction templates for
the LAMMPS REACTION package, leveraging graph-theoretical
principles and subgraph isomorphism techniques. By representing molecular
systems as mathematical graphs, the method enables the automated identification
of conserved molecular domains, reaction sites, and atom mappings,
significantly reducing manual effort. The algorithm was validated
on three case studies: poly addition, poly condensation, and chain
polymerization, demonstrating its ability to map conserved domains,
identify reaction-initiating atoms, and resolve challenges such as
symmetric reactants and indistinguishable atoms. Additionally, the
generated templates were optimized for computational efficiency by
retaining only essential reactive domains, ensuring scalability and
consistency in high-throughput workflows for computational chemistry,
materials science, and machine learning applications. Future work
will focus on extending the method to mixed organic–inorganic
systems, incorporating adaptive scoring mechanisms, and integrating
quantum mechanical calculations to enhance its applicability.