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Understanding bond rupture in polymer networks remains a fundamental challenge due to the interplay of network topology and condensed phase effects. In this work, we introduce a predictive approach for determining bond rupture in thermosets based on shortest paths (SPs) analysis of the network topology. This method enumerates SP sets in networks with periodic boundary conditions, with applications to both all-atom and coarse-grained simulations. We find that bond rupture is most likely to initiate on the first (shortest) SP in the thermoset network and the strain at which the first bond ruptures is linearly correlated with the topological path length. As a result, one can predict the first bond rupture by computing the first SP directly from the initial thermoset topology without resorting to MD simulations. Furthermore, the specific bond rupture location along the first SP follows a probability distribution associated with the SP-based betweenness centrality. Subsequent bond rupture events are dictated by the instantaneous SP of partially broken networks. Moreover, we quantify the length scale dependence of SP distributions, introducing a means of partially bridging the observed ductile rupture in molecular simulations and brittle rupture in experiments. 

In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from ab initio calculations in amorphous structures. The local feature representation is specifically chosen to make the linear weights a force field as a force/potential function of the atom pair distance. Consequently, this set of functions is the closest representation of the ab initio forces, given the two-body approximation and finite scanning in the configurational space. We validate this approach in amorphous silica. Potentials in the new force field (consisting of tabulated Si–Si, Si–O, and O–O potentials) are significantly different than existing potentials that are commonly used for silica, even though all of them produce the tetrahedral network structure and roughly similar glass properties. This suggests that the commonly used classical force fields do not offer fundamentally accurate representations of the atomic interaction in silica. The new force field furthermore produces a lower glass transition temperature (Tg ∼ 1800 K) and a positive liquid thermal expansion coefficient, suggesting the extraordinarily high Tg and negative liquid thermal expansion of simulated silica could be artifacts of previously developed classical potentials. Overall, the proposed approach provides a fundamental yet intuitive way to evaluate two-body potentials against ab initio calculations, thereby offering an efficient way to guide the development of classical force fields. 

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.