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Reactive machine-learned potentials for fluoropolymer binders: unified physical and chemical property validation
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DOI：10.1039/D6CP01325B.png)
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Abstract
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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 (Tg); 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 Tg; 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.
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