Reactive Machine-Learned Potentials for Fluoropolymer Binders: Unified Physical and Chemical Property Validation

Huanyu Zhu; Mingjie Wen; Dongping Chen; Qingzhao Chu

doi:10.1039/d6cp01325b

January 1, 2026Physical Chemistry Chemical Physics

Reactive Machine-Learned Potentials for Fluoropolymer Binders: Unified Physical and Chemical Property Validation

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Key Points

The central aim is to develop a force field for accurate simulations of polymer-bonded explosives during initiation processes.
Developed machine-learned potentials to model fluoropolymer binders.
Validated physical and chemical properties of the proposed force field through atomistic simulations.
Tested under conditions simulating mechanical deformation and thermal response.
The new force field accurately captures both mechanical deformation and thermal response of fluoropolymer binders.
Chemical bond scission events were effectively modeled, showing a strong correlation with empirical data.

Implication

Demonstrates accurate simulation of fluoropolymer properties, indicating improved PBX performance.

Abstract

Accurate atomistic simulation of polymer-bonded explosive (PBX) initiation demands a force field that simultaneously captures mechanical deformation, thermal response, and chemical bond scission-yet no such description has existed for fluoropolymer...

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Cite This Study

Zhu et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf6aefaef96ed7f058590 https://doi.org/10.1039/d6cp01325b

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Reactive Machine-Learned Potentials for Fluoropolymer Binders: Unified Physical and Chemical Property Validation

Key Points

Implication

Abstract

Discussion

Cite This Study

Reactive Machine-Learned Potentials for Fluoropolymer Binders: Unified Physical and Chemical Property Validation

Key Points

Implication

Abstract

Discussion

Cite This Study

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