Despite growing interest in polymers under extreme conditions, most atomistic molecular dynamics simulations cannot describe the bond scission events underlying failure modes in polymer networks undergoing large strains. In this work, we propose a physics-based machine learning approach that can detect and perform bond breaking with near quantum-chemical accuracy on-the-fly in atomistic simulations. Particularly, we demonstrate that by coarse-graining highly correlated neighboring bonds, the prediction accuracy can be dramatically improved. By comparing with existing quantum mechanics/molecular mechanics methods, our approach is approximately two orders of magnitude more efficient and exhibits improved sensitivity toward rare bond breaking events at low strain. The proposed bond breaking molecular dynamics scheme enables fast and accurate modeling of strain hardening and material failure in polymer networks and can accelerate the design of polymeric materials under extreme conditions.
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Machine learning quantum-chemical bond scission in thermosets under extreme deformation
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22 May 2023
Research Article|
May 25 2023
Machine learning quantum-chemical bond scission in thermosets under extreme deformation
Zheng Yu
;
Zheng Yu
(Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing)
Department of Chemistry, University of Illinois at Urbana-Champaign
, Urbana, Illinois 61801, USA
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Nicholas E. Jackson
Nicholas E. Jackson
a)
(Conceptualization, Supervision, Writing – original draft, Writing – review & editing)
Department of Chemistry, University of Illinois at Urbana-Champaign
, Urbana, Illinois 61801, USA
a)Author to whom correspondence should be addressed: [email protected]
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a)Author to whom correspondence should be addressed: [email protected]
Note: This paper is part of the APL Special Collection on Accelerate Materials Discovery and Phenomena.
Appl. Phys. Lett. 122, 211906 (2023)
Article history
Received:
March 11 2023
Accepted:
May 11 2023
Citation
Zheng Yu, Nicholas E. Jackson; Machine learning quantum-chemical bond scission in thermosets under extreme deformation. Appl. Phys. Lett. 22 May 2023; 122 (21): 211906. https://doi.org/10.1063/5.0150085
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