Modeling Exchange Reactions in Covalent Adaptable Networks with Machine Learning Force Fields

Abstract

Recycling and reprocessing of conventional thermosetting polymers have received considerable attention in view of environmental protection and sustainable development. By incorporating specific functional groups capable of reversible exchange reactions into polymer networks, the covalent adaptable networks (CANs) can alter the topology arrangement and achieve stress relaxation. Studying the topology rearrangement using conventional empirical force fields is challenging since they have a fixed bond connectivity. Ab initio molecular dynamics is capable of describing the exchange reactions, but it cannot study the kinetics that exceeds the achievable time scale. To address these problems, we developed a machine-learning force field for the exchange reactions of polyimine CANs. We showed that the developed machine-learning force field can achieve DFT-level accuracy in energy and atomic force predictions. By combining the developed machine learning force field with enhanced sampling methods, we provided a description of the reaction mechanisms and quantified the corresponding free energy profiles, revealing how water molecules affect the amine-imine exchange reactions, which cannot be achieved by conventional empirical force fields with fixed bond connectivity. Additionally, we illustrated the exchange reactioninduced topology rearrangement and relaxation of local stress in polyimine CANs using the developed machine learning force field.

Trapping bond exchange phenomenon revealed for off-stoichiometry cross-linking of phase-separated vitrimer-like materials

“…The relaxation dynamics are known to be dominated by the bond exchange rate in a given system. 32–34 In the present system, the dynamics should be determined by the frequency of inter-domain bond exchange. Thus, if the bond exchange is trapped in the matrix, the relaxation rate is naturally slowed.…”

Molecular Simulation of Covalent Adaptable Networks and Vitrimers: A Review

“…All previous simulations efforts were based on empirical atomistic force fields, such as CVFF and PCFF, or ReaxFF. However, very recently, a machine-learning (ML) force field (FF) based on density functional theory (DFT) ab initio simulations [50] was developed for polyimine CAN systems (specifically based on dialdehyde and diamine monomers) in order to obtain DFT-level accuracy in energy and atomic force predictions [69]. By combining the ML force field with enhanced sampling methods, including metadynamics and umbrella sampling, the free energy profiles of amine-imine exchange reactions in networks, both with and without water molecules, were calculated [69].…”

“…However, very recently, a machine-learning (ML) force field (FF) based on density functional theory (DFT) ab initio simulations [50] was developed for polyimine CAN systems (specifically based on dialdehyde and diamine monomers) in order to obtain DFT-level accuracy in energy and atomic force predictions [69]. By combining the ML force field with enhanced sampling methods, including metadynamics and umbrella sampling, the free energy profiles of amine-imine exchange reactions in networks, both with and without water molecules, were calculated [69]. In particular, the ML force field described the change in chain connectivity and stress distribution induced by amine-imine exchange reactions and reproduced reaction kinetics and transition state geometries that could not be achieved by empirical force fields [69].…”

“…By combining the ML force field with enhanced sampling methods, including metadynamics and umbrella sampling, the free energy profiles of amine-imine exchange reactions in networks, both with and without water molecules, were calculated [69]. In particular, the ML force field described the change in chain connectivity and stress distribution induced by amine-imine exchange reactions and reproduced reaction kinetics and transition state geometries that could not be achieved by empirical force fields [69].…”