Implicit Chain Particle Model for Polymer-Grafted Nanoparticles

Matrix-free nanocomposites made from polymer-grafted nanoparticles (PGN) represent a paradigm shift in materials science because they greatly improve nanoparticle dispersion and offer greater tunability over rheological mechanical properties comparison to neat polymers. Utilizing the full potential of PGNs requires deeper understanding how polymer graft length, density, chemistry influence interfacial interactions between particles. There has been great progress describing these effects with molecular dynamics (MD). However, limitations length time scales MD make it prohibitively costly study systems involving more than few PGNs, even bead–spring coarse-grained models. Moreover, remains unclear properly address shortcomings models rate-dependent constitutive response polymers chemistry-specific fashion. Here, we some challenges by proposing new modeling for using strain-energy mapping framework mean force (PMF) calculations. In this approach, each is coarse grained into representative particle chains treated implicitly, namely, implicit chain model (ICPM). Using (CG-MD) poly(methyl methacrylate) as testbed, derive effective interaction particles arranged close-packed lattice configuration matching bulk dilation compression densities up failure. We establish an iterative optimization scheme fine tune PMFs ICPM accurately match stress–strain behaviors during tests. The strain-rate dependence work done quantified reveal that interparticle can be expressed strain-rate-dependent energy well depth culminates simple power-law Cowper–Symonds strain hardening model. Given aggressive degree graining (∼1:10 000) involved, scope are cautiously discussed. Overall, increases computational speed approximately 5–6 orders magnitude compared CG-MD This novel foundational particle-based simulations their blends accelerates predictions emergent PGN materials.