Reactive machine-learned potentials for fluoropolymer binders: unified physical and chemical property validation

Accurate atomistic simulation of polymer-bonded explosive (PBX) initiation demands a force field that simultaneously captures mechanical deformation, thermal response, and chemical bond scission. Classical force fields reproduce equilibrium physical properties but cannot describe bond breaking, and ReaxFF contains no parameterization for F23-series fluoropolymers, leaving binder degradation and explosive decomposition inaccessible to molecular simulations. Here, we report a reactive machine learning potential (MLP) for three fluoropolymer binders widely used in PBXs: F2311, F2313, and F2314. Trained on density functional theory (DFT) datasets via the deep potential framework with iterative active learning, the MLP achieves ab initio accuracy at a fraction of the computational cost. Validation spans both physical and chemical regimes: radial distribution functions, equilibrium densities, glass transition temperature (T_g), and elastic constants agree quantitatively with experiment and ab initio molecular dynamics benchmarks. A microscopic analysis of backbone torsional transition events further reveals a positive correlation between dihedral activation energy and T_g, providing atomic-scale mechanistic insight into their thermal-mechanical behavior. Critically, the MLP faithfully reproduces DFT atomic forces throughout bond scission under uniaxial tension with bond dissociation energies deviating only 0.14-0.15 eV from DFT reference values. This work provides a foundation for future atomistic studies of PBX initiation, which fills a critical gap in reactive force field coverage for F23-series fluoropolymer binders and establishes a validated framework spanning physical and reactive property regimes.