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
- Reactive Machine Learning Force Field for Crosslinked Epoxy
- 高分子科学（英文） 2025年43卷第10期页码：1761-1773
- 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：
  
  This work was financially supported by the National Natural Science Foundation of China (Nos. 52303116 and 52403125), the Natural Science Foundation of Hunan Province (No. 2024JJ6461), the Science and Technology Innovation Program of Hunan Province (Nos. 2022RC1080 and 2023RC3006), the Innovation Research Foundation of NUDT (Nos. 22-ZZCX-076 and 23-ZZCX-ZZGC-01-10), and the Key Research and Development Program of Hunan Province of China (No. 2023ZJ1040).;Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-025-3389-4.
- DOI：10.1007/s10118-025-3389-4
  中图分类号：
- 收稿：2025-04-02，
  
  修回：2025-05-16，
  
  录用：2025-05-28，
  
  网络首发：2025-07-29，
  
  纸质出版：2025-10-05
- 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

Li, J.; Liu, X.; Feng, Y.; Yin, J. Recent progress in polymer/two-dimensional nanosheets composites with novel performances. Prog. Polym. Sci. 2022 , 126 , 101505..

Huang, H. D.; Ren, P. G.; Zhong, G. J.; Olah, A.; Li, Z. M.; Baer, E.; Zhu, L. Promising strategies and new opportunities for high barrier polymer packaging films. Prog. Polym. Sci. 2023 , 144 , 101722..

Zheng, H.; Zhang, W.; Li, B.; Zhu, J.; Wang, C.; Song, G.; Wu, G.; Yang, X.; Huang, Y.; Ma, L. Recent advances of interphases in carbon fiber-reinforced polymer composites: a review. Compos. Part B 2022 , 233 , 109639..

Wu, W.; Feng, H.; Xie, L.; Zhang, A.; Liu, F.; Liu, Z.; Zheng, N.; Xie, T. Reprocessable and ultratough epoxy thermosetting plastic. Nat. Sustain. 2024 , 7 , 804−811..

Han, X.; Yuan, L.; Gu, A.; Liang, G. Development and mechanism of ultralow dielectric loss and toughened bismaleimide resins with high heat and moisture resistance based on unique amino-functionalized metal-organic frameworks. Compos. Part B 2018 , 132 , 28−34..

Li, X.; Zhang, P.; Dong, J.; Gan, F.; Zhao, X.; Zhang, Q. Preparation of low-κ polyimide resin with outstanding stability of dielectric properties versus temperature by adding a reactive Cardo-containing diluent. Compos. Part B 2019 , 177 , 107401..

Volksen, W.; Miller, R. D.; Dubois, G. Low dielectric constant materials. Chem. Rev. 2010 , 110 , 56−110..

Qiu, H.; Sun, Z. Y. On-demand reverse design of polymers with PolyTAO. npj Comput. Mater. 2024 , 10 , 273..

Chen, L.; Kim, C.; Batra, R.; Lightstone, J. P.; Wu, C.; Li, Z.; Deshmukh, A. A.; Wang, Y.; Tran, H. D.; Vashishta, P.; Sotzing, G. A.; Cao, Y.; Ramprasad, R. Frequency-dependent dielectric constant prediction of polymers using machine learning. npj Comput. Mater. 2020 , 6 , 61..

Wang, M.; Xu, X.; Ji, J.; Yang, Y.; Shen, J.; Ye, M. The hygrothermal aging process and mechanism of the novolac epoxy resin. Compos. Part B 2016 , 107 , 1−8..

Liao, D.; Gu, T.; Liu, J.; Chen, S.; Zhao, F.; Len, S.; Dou, J.; Qian, X.; Wang, J. Degradation behavior and ageing mechanism of E-glass fiber reinforced epoxy resin composite pipes under accelerated thermal ageing conditions. Compos. Part B 2024 , 270 , 111131..

Mammone, M.; Panta, J.; Mildren, R. P.; Wang, J.; Escobedo-Diaz, J.; McGarva, L.; Ibrahim, M.; Sharp, A.; Yang, R.; Zhang, Y. X. Advanced characterization of thermal degradation mechanisms in carbon fibre-reinforced polymer composites under continuous wave laser irradiation. Compos. Part A 2025 , 192 , 108817..

Hayashi, Y.; Shiomi, J.; Morikawa, J.; Yoshida, R. RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics. npj Comput. Mater. 2022 , 8 , 222..

Winetrout, J. J.; Kanhaiya, K.; Kemppainen, J.; in ‘t Veld, P. J.; Sachdeva, G.; Pandey, R.; Damirchi, B.; van Duin, A.; Odegard, G. M.; Heinz, H. Implementing reactivity in molecular dynamics simulations with harmonic force fields. Nat. Commun. 2024 , 15 , 7945..

Miyata, T.; Sato, Y. K.; Kawagoe, Y.; Shirasu, K.; Wang, H. F.; Kumagai, A.; Kinoshita, S.; Mizukami, M.; Yoshida, K.; Huang, H. H.; Okabe, T.; Hagita, K.; Mizoguchi, T.; Jinnai, H. Effect of inorganic material surface chemistry on structures and fracture behaviours of epoxy resin. Nat. Commun. 2024 , 15 , 1898..

Zhang, J.; Jiang, C.; Deng, G.; Luo, M.; Ye, B.; Zhang, H.; Miao, M.; Li, T.; Zhang, D. Closed-loop recycling of tough epoxy supramolecular thermosets constructed with hyperbranched topological structure. Nat. Commun. 2024 , 15 , 4869..

Sun, H.; Mumby, S. J.; Maple, J. R.; Hagler, A. T. An ab initio CFF93 all-atom force field for polycarbonates. J. Am. Chem. Soc. 1994 , 116 , 2978−2987..

Weiner, P. K.; Kollman, P. A. AMBER: assisted model building with energy refinement. A general program for modeling molecules and their interactions. J. Comput. Chem. 1981 , 2 , 287−303..

Dauber-Osguthorpe, P.; Roberts, V. A.; Osguthorpe, D. J.; Wolff, J.; Genest, M.; Hag ler, A. T. Structure and energetics of ligand binding to proteins: escherichia coli dihydrofolate reductase-trimethoprim, a drug-receptor system. Proteins: Struct., Funct., Bioinf. 1988 , 4 , 31−47..

Sun, H. COMPASS: an ab initio force-field optimized for condensed-phase applications overview with details on alkane and benzene compounds. J. Phys. Chem. B 1998 , 102 , 7338−7364..

Van Duin, A. C. T.; Dasgupta, S.; Lorant, F.; Goddard, W. A. ReaxFF: a reactive force field for hydrocarbons. J. Phys. Chem. A 2001 , 105 , 9396−9409..

Senftle, T. P.; Hong, S.; Islam, M. M.; Kylasa, S. B.; Zheng, Y.; Shin, Y. K.; Junkermeier, C.; Engel-Herbert, R.; Janik, M. J.; Aktulga, H. M.; Verstraelen, T.; Grama, A.; van Duin, A. C. T. The ReaxFF reactive force-field: development, applications and future directions. npj Comput. Mater. 2016 , 2 , 15011..

Xu, M. X.; Di, J. Y.; Wu, Y. C.; Meng, X. X.; Haiwen, J.; Jiang, H.; Li, J. H.; Lu, Q. Insights into the pyrolysis mechanisms of epoxy resin polymers based on the combination of experiments and ReaxFF-MD simulation. Chem. Eng. J. 2023 , 473 , 145404..

Chenoweth, K.; van Duin, A. C. T.; Goddard, W. A. ReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidation. J. Phys. Chem. A 2008 , 112 , 1040−1053..

Moerman, E.; Furman, D.; Wales, D. J. Systematic evaluation of ReaxFF reactive force fields for biochemical applications. J. Chem. Theory Comput. 2021 , 17 , 497−514..

Vashisth, A.; Ashraf, C.; Zhang, W.; Bakis, C. E.; van Duin, A. C. T. Accelerated ReaxFF simulations for describing the reactive cross-linking of polymers. J. Phys. Chem. A 2018 , 122 , 6633−6642..

Ko, T. W.; Ong, S. P. Recent advances and outstanding challenges for machine learning interatomic potentials. Nat. Comput. Sci. 2023 , 3 , 998−1000..

Röcken, S.; Zavadlav, J. Accurate machine learning force fields via experimental and simulation data fusion. npj Comput. Mater. 2024 , 10 , 69..

Sha, W.; Li, Y.; Tang, S.; Tian , J.; Zhao, Y.; Guo, Y.; Zhang, W.; Zhang, X.; Lu, S.; Cao, Y. C.; Cheng, S. Machine learning in polymer informatics. InfoMat 2021 , 3 , 353−361..

Hong, S. J.; Chun, H.; Lee, J.; Kim, B. H.; Seo, M. H.; Kang, J.; Han, B. First-principles-based machine-learning molecular dynamics for crystalline polymers with van der waals interactions. J. Phys. Chem. Lett. 2021 , 12 , 6000−6006..

Sun, Y.; Wan, K.; Shen, W.; He, J.; Zhou, T.; Wang, H.; Yang, H.; Shi, X. Modeling exchange reactions in covalent adaptable networks with machine learning force fields. Macromolecules 2023 , 56 , 9003−9013..

Hedman, D.; McLean, B.; Bichara, C.; Maruyama, S.; Larsson, J. A.; Ding, F. Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations. Nat. Commun. 2024 , 15 , 4076..

Zeng, J.; Cao, L.; Xu, M.; Zhu, T.; Zhang, J. Z. H. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation. Nat. Commun. 2020 , 11 , 5713..

Park, C. W.; Kornbluth, M.; Vandermause, J.; Wolverton, C.; Kozinsky, B.; Mailoa, J. P. Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture. npj Comput. Mater. 2021 , 7 , 73..

Chang, X.; Chu, Q.; Chen, D. Monitoring the melting behavior of boron nanoparticles using a neural network potential. Phys. Chem. Chem. Phys. 2023 , 25 , 12841−12853..

Wen, M.; Shi, J.; Chang, X.; Han, J.; Pang, K.; Chen, D.; Chu, Q. Uncovering the decomposition mechanism of nitrate ester plasticized polyether (NEPE): a neural network potential simulation. Phys. Chem. Chem. Phys. 2024 , 26 , 25719−25730..

Sun, H.; Zhang, C.; Tang, L.; Wang, R.; Xia, W.; Wang, C.-Z. Molecular dynamics simulation of Fe-Si alloys using a neural network machine learning potential. Phys. Rev. B 2023 , 107 , 224301..

Zhang, Y.; Ouyang, X.; Fang, D.; Hu, S.; Liu, L.; Wang, D. Machine-learning modeling of elemental ferroelectric bismuth monolayer. Phys. Rev. Lett. 2024 , 133 , 266103..

Chahal, R.; Toomey, M. D.; Kearney, L. T.; Sedova, A.; Damron, J. T.; Naskar, A. K.; Roy, S. Deep-learning interatomic potential connects molecular structural ordering to the macroscale properties of polyacrylonitrile. ACS Appl. Mater. Interfaces 2024 , 16 , 36878−36891..

Williams, C. D.; Kalayan, J.; Burton, N. A.; Bryce, R. A. Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials. Chem. Sci. 2024 , 15 , 12780−12795..

Long, T.; Li, J.; Wang, C.; Wang, H.; Cheng, X.; Lu, H.; Zhang, Y.; Zhou, C. Polymers simulation using machine learning interatomic potentials. Polymer 2024 , 308 , 127416..

Allen, A. E. A.; Lubbers, N.; Matin, S.; Smith, J.; Messerly, R.; Tretiak, S.; Barros, K. Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning. npj Comput. Mater. 2024 , 10 , 154..

Erhard, L. C.; Rohrer, J.; Albe, K.; Deringer, V.L. A machine-learned interatomic potential for silica and its relation to empirical models. npj Comput. Mater. 2022 , 8 , 90..

Yoo, P.; Sakano, M.; Desai, S.; Islam, M. M.; Liao, P.; Strachan, A. Neural network reactive force field for C, H, N, and O systems. npj Comput. Mater. 2021 , 7 , 9..

Unke, O. T.; Chmiela, S.; Sauceda, H. E.; Gastegger, M.; Poltavsky, I.; Schütt, K. T.; Tkatchenko, A.; Müller, K. R. Machine learning force fields. Chem. Rev. 2021 , 121 , 10142−10186..

Wieser, S.; Zojer, E. Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks. npj Comput. Mater. 2024 , 10 , 18..

Wen, M.; Chang, X.; Xu, Y.; Chen, D.; Chu, Q. Determining the mechanical and decomposition properties of high energetic materials ( α -RDX, β -HMX, and ε -CL-20) using a neural network potential. Phys. Chem. Chem. Phys. 2024 , 26 , 9984−9997..

[Hutter, J.; Iannuzzi, M.; Schiffmann, F.; VandeVondele, J. cp2k: atomistic simulations of condensed matter systems. WIREs Comput. Mol. Sci . 2014, 4 , 15-25..

Kühne, T. D.; Iannuzzi, M.; Del Ben, M.; Rybkin, V. V.; Seewald, P.; Stein, F.; Laino, T.; Khaliullin, R. Z.; Schütt, O.; Schiffmann, F.; Golze, D.; Wilhelm, J.; Chulkov, S.; Bani-Hashemian, M. H.; Weber, V.; Borštnik, U.; Taillefumier, M.; Jakobovits, A. S.; Lazzaro, A.; Pabst, H.; Müller, T.; Schade, R.; Guidon, M.; Andermatt, S.; Holmberg, N.; Schenter, G. K.; Hehn, A.; Bussy, A.; Belleflamme, F.; Tabacchi, G.; Glöß, A.; Lass, M.; Bethune, I.; Mundy, C. J.; Plessl, C.; Watkins, M.; VandeVondele, J.; Krack, M.; Hutter, J. CP2K: An electronic structure and molecular dynamics software package - quickstep: efficient and accurate electronic structure calculations. J. Chem. Phys . 2020 , 152 , 194103..

Goedecker, S.; Teter, M.; Hutter, J. Separable dual-space Gaussian pseudopotentials. Phys. Rev. B 1996 , 54 , 1703−1710..

Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996 , 77 , 3865−3868..

Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys . 2010 , 132 , 154104..

VandeVondele, J.; Hutter, J. Gaussian basis sets for accurate calculations on molecular systems in gas and condensed phases. J. Chem. Phys . 2007 , 127 , 114105..

Wang, H.; Zhang, L.; Han, J.; E, W. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 2018 , 228 , 178−184..

Zeng, J.; Zhang, D.; Lu, D.; Mo, P.; Li, Z.; Chen, Y.; Rynik, M.; Huang, L. a.; Li, Z.; Shi, S.; Wang, Y.; Ye, H.; Tuo, P.; Yang, J.; Ding, Y.; Li, Y.; Tisi, D.; Zeng, Q.; Bao, H.; Xia, Y.; Huang, J.; Muraoka, K.; Wang, Y.; Chang, J.; Yuan, F.; Bore, S. L.; Cai, C.; Lin, Y.; Wang, B.; Xu, J.; Zhu, J. X.; Luo, C.; Zhang, Y.; Goodall, R. E. A.; Liang, W.; Singh, A. K.; Yao, S.; Zhang, J.; Wentzcovitch, R.; Han, J.; Liu, J.; Jia, W.; York, D. M.; E, W.; Car, R.; Zhang, L.; Wang, H. DeePMD-kit v2: a software package for deep potential models. J. Chem. Phys . 2023 , 159 , 054801..

Plimpton, S. Fast Parallel Algorithms for Short-Range Molecular Dynamics. J. Comput. Phys. 1995 , 117 , 1−19..

Evans, D. J.; Holian, B. L. The Nose–Hoover thermostat. J. Chem. Phys. 1985 , 83 , 4069−4074..

Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 1981 , 52 , 7182−7190..

Martyna, G. J.; Tobias, D. J.; Klein, M. L. Constant pressure molecular dynamics algorithms. J. Chem. Phys. 1994 , 101 , 4177−4189..

Shinoda, W.; Shiga, M.; Mikami, M. Rapid estimation of elastic constants by molecular dynamics simulation under constant stress. Phys. Rev. B 2004 , 69 , 134103..

Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO–the open visualization tool. Modell. Simul. Mater. Sci. Eng. 2010 , 18 , 015012..

[Liu, L.; Liu, Y.; Zybin, S. V.; Sun, H.; Goddard, W. A., III ReaxFF-lg: correction of the ReaxFF reactive force field for london dispersion, with applications to the equations of state for energetic materials. J. Phys. Chem. A 2011, 115 , 11016−11022..

Varshney, V.; Patnaik, S. S.; Roy, A. K.; Farmer, B. L. A Molecular dynamics study of epoxy-based networks: cross-linking procedure and prediction of molecular and material properties. Macromolecules 2008 , 41 , 6837−6842..

Arruda, E. M.; Boyce, M. C.; Jayachandran, R. Effects of strain rate, temperature and thermomechanical coupling on the finite strain deformation of glassy polymers. Mech. Mater. 1995 , 19 , 193−212..

Abdul Manap, A. H.; Md Izah, S. S.; Mohamed, K. Molecular dynamics study of poly(dimethylsiloxane) nanostructure distortion in a soft lithography demolding process. ACS Omega 2019 , 4 , 20257−20264..

Hasheminejad, K.; Montazeri, A. Enhanced interfacial characteristics in PLA/graphene composites through numerically-designed interface treatment. Appl. Surf. Sci. 2020 , 502 , 144150..

Bethke, C.; Goller, S. M.; Ngoc, U. L. D.; Kaysser, S. T.; Altstädt, V.; Ruckdäschel, H. Tailoring epoxy resin foams by pre-curing with neat amine hardeners and its derived carbamates. Polymers 2021 , 13 , 1348..

Kobashi, K.; Nishino, H.; Yamada, T.; Futaba, D. N.; Yumura, M.; Hata, K. Epoxy composite sheets with a large interfacial area from a high surface area-supplying single-walled carbon nanotube scaffold filler. Carbon 2011 , 49 , 5090−5098..

Ardanuy, M.; Rodríguez-Perez, M. A.; Algaba, I. Electrical conductivity and mechanical properties of vapor-grown carbon nanofibers/trifunctional epoxy composites prepared by direct mixing. Compos. Part B 2011 , 42 , 675−681..

Xie, J. Z.; Zhou, X. Y.; Jin, B.; Jiang, H. Machine learning force field-aided cluster expansion approach to phase diagram of alloyed materials. J. Chem. Theory Comput. 2024 , 20 , 6207−6217..

D’Astolfo, P.; Wang, X.; Liu, X.; Kisiel, M.; Drechsel, C.; Baratoff, A.; Aschauer, U.; Decurtins, S.; Liu, S. X.; Pawlak, R.; Meyer, E. Energy dissipation from confined states in nanoporous molecular networks. ACS Nano 2022 , 16 , 16314−16321..

Lu, X.; Jiang, B. Glass transition temperature and molecular parameters of polymer. Polymer 1991 , 32 , 471−478..

Fryer, D. S.; Peters, R. D.; Kim, E. J.; Tomaszewski, J. E.; de Pablo, J. J.; Nealey, P. F.; White, C. C.; Wu, W. L. Dependence of the glass transition temperature of polymer films on interfacial energy and thickness. Macromolecules 2001 , 34 , 5627−5634..

Lin, K.-H.; Paterson, L.; May, F.; Andrienko, D. Glass transition temperature prediction of disordered molecular solids. npj Comput. Mater. 2021 , 7 , 179..

Sindt, O.; Perez, J.; Gerard, J. F. Molecular architecture-mechanical behaviour relationships in epoxy networks. Polymer 1996 , 37 , 2989−2997..

Yu, Z.; Jackson, N. E. Exploring thermoset fracture with a quantum chemically accurate model of bond scission. Macromolecules 2024 , 57 , 1414−1425..

Miura, T.; Shimoi, Y.; Hasegawa, K.; Takagi, K.; Yamazaki, N.; Matsuki, T.; Harada, Y.; Ohkubo, M. Simulation study of the effects of interfacial bonds on adhesion and fracture behavior of epoxy resin layers. J. Phys. Chem. B 2021 , 125 , 11 044−11057..

Won, Y. G.; Galy, J.; Gérard, J. F.; Pascault, J. P.; Bellenger, V.; Verdu, J. Internal antiplasticization in copolymer and terpolymer networks based on diepoxides, diamines and monoamines. Polymer 1990 , 31 , 1787−1792..

Roche, A. A.; Bouchet, J.; Bentadjine, S. Formation of epoxy-diamine/metal interphases. Int. J. Adhes. Adhes. 2002 , 22 , 431−441..

Garcia, F. G.; Soares, B. G.; Pita, V. J. R. R.; Sánchez, R.; Rieumont, J. Mechanical properties of epoxy networks based on DGEBA and aliphatic amines. J. Appl. Polym. Sci. 2007 , 106 , 2047−2055..

Fan, C. F.; Cagin, T.; Chen, Z. M.; Smith, K. A. Molecular modeling of polycarbonate. 1. Force field, static structure, and mechanical properties. Macromolecules 1994 , 27 , 2383−2391..

Bouchet, J.; Roche, A. A.; Hamelin, P. Internal stresses, Young’s modulus and practical adhesion of organic coatings applied onto 5754 aluminium alloy. Thin Solid Films 1999 , 355-356 , 270−276..

Ahrens, A.; Bonde, A.; Sun, H.; Wittig, N. K.; Hammershøj, H. C. D.; Batista, G. M. F.; Sommerfeldt, A.; Frølich, S.; Birkedal, H.; Skrydstrup, T. Catalytic disconnection of C–O bonds in epoxy resins and composites. Nature 2023 , 617 , 730−737..

St. John, P. C.; Guan, Y.; Kim, Y.; Kim, S.; Paton, R. S. Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nat. Commun. 2020 , 11 , 2328..

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