Reactive Machine Learning Force Field for Crosslinked Epoxy

Jun-Shan Si; Nan Wu; Ming-Jie Wen; Dong-Ping Chen; Yong-Lyu He; Jian-Wei Zhang; Ke Duan

doi:10.1007/s10118-025-3389-4

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Reactive Machine Learning Force Field for Crosslinked Epoxy

RESEARCH ARTICLE | Updated：2025-09-26

- Reactive Machine Learning Force Field for Crosslinked Epoxy
- Chinese Journal of Polymer Science Vol. 43, Issue 10, Pages: 1761-1773(2025)
- Affiliations：
  
  a.College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
  b.State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, China
- Author bio：
  
  jianwei_zhang@nudt.edu.cn (J.W.Z.)
  duank_1128@nudt.edu.cn (K.D.)
- Funds：
- DOI：10.1007/s10118-025-3389-4
  CLC：
- Received：02 April 2025，
  
  Revised：2025-05-16，
  
  Accepted：28 May 2025，
  
  Online First：29 July 2025，
  
  Published：05 October 2025
- Accepted：
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Si, J. S.; Wu, N.; Wen, M. J.; Chen, D. P.; He, Y. L.; Zhang, J. W.; Duan, K. Reactive machine learning force field for crosslinked epoxy. Chinese J. Polym. Sci. 2025, 43, 1761–1773

Jun-Shan Si, Nan Wu, Ming-Jie Wen, et al. Reactive Machine Learning Force Field for Crosslinked Epoxy[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1761-1773.
Si, J. S.; Wu, N.; Wen, M. J.; Chen, D. P.; He, Y. L.; Zhang, J. W.; Duan, K. Reactive machine learning force field for crosslinked epoxy. Chinese J. Polym. Sci. 2025, 43, 1761–1773 DOI： 10.1007/s10118-025-3389-4.

Jun-Shan Si, Nan Wu, Ming-Jie Wen, et al. Reactive Machine Learning Force Field for Crosslinked Epoxy[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1761-1773. DOI： 10.1007/s10118-025-3389-4.

摘要

The work presents a reactive machine learning force field for crosslinked epoxy

capable of accurately predicting reactive events and thermo-mechanical properties. The force field operates approximately 1200 times faster than ab initio molecular dynamics

offering a promising approach for simulating complex polymer systems with quantum-level precision.

Abstract

Large-scale molecular dynamics (MD) simulations of crosslinked epoxy with quantum-level accuracy while capturing complex reactivity is a compelling yet unrealized challenge. In this work

through the construction of a chemical-environment-directing dataset

a reactive machine learning force field that accurately captures both reactive events and thermos-mechanical properties is developed. The force field achieves energy and force root-mean-square errors of 1.3 meV/atom and 159 meV/Å

respectively

and operates approximately 1200 times faster than ab initio molecular dynamics. MD simulations demonstrate excellent predictive capabilities across multiple critical thermos-mechanical properties (radial distribution function

density

and elastic modulus)

with results being well consistent with experimental values. In particular

the force field can provide accurate prediction of the bond dissociation energies for typical bonds with a mean absolute error of 7.8 kcal/mol (<8%)

which enables the simulation of tensile-induced failure caused by chemical bond breaking. Our work demonstrates the capability of the machine learning force field to handle the extraordinary complexity of crosslinked epoxy systems

providing a valuable blueprint for future development of more generalized reactive force fields applicable to most polymers.

关键词

Keywords

references

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