ViscoNet: A lightweight FEA surrogate model for polymer nanocomposites viscoelastic response prediction

Polymer-based nanocomposites (PNCs) are formed by dispersing nanoparticles (NPs) within a polymer matrix, which creates polymer interphase regions that drive property enhancement. However, data-driven PNC design is challenging due to limited data. To address the challenge, we present ViscoNet, a surrogate model for finite element analysis (FEA) simulations of PNC viscoelastic (VE) response. ViscoNet leverages pre-training and finetuning to accelerate predicting VE response of a new PNC system. By predicting the entire VE response, ViscoNet surpasses previous scalar-based surrogate models for FEA simulation, offering better fidelity and efficiency. We explore ViscoNet's effectiveness through generalization tasks, both within thermoplastics and from thermoplastics to thermosets, reporting a mean absolute percentage error (MAPE) of < 5 % for rubbery modulus and < 1 % for glassy modulus in all cases and 1.22 % on tan δ peak height prediction. With only 500 FEA simulations for finetuning, ViscoNet can generate over 20k VE responses within 2 min with 1 CPU, compared to 97 days with 4 CPUs via FEA simulations.