Extending Simulations of Low-Temperature Polymer Melt Phase Behavior to Large Molecular Weight

Hundreds of millions of tons of semi-crystalline polymers are produced annually, yet the molecular mechanisms of polymer crystallization remain a subject of debate. A key open question concerns how low-temperature phase behavior influences primary nucleation in polymer melts. Our previous work employed advanced sampling Monte Carlo methods, including Wang-Landau (WL) simulations, to compute phase diagrams relevant for crystallization. In extending these simulations to larger systems and higher molecular weights, we found that WL simulations become prohibitively slow at low temperatures--independent of the system relaxation time--necessitating an alternative approach. To overcome the slow dynamics near the ground state, we apply the Stochastic Approximation Monte Carlo (SAMC) method to study polymer melt crystallization for the first time. We compare its performance with WL and assess its scalability with system size. Finally, we present our most recent results for larger systems and longer chains, highlighting finite-size effects where applicable.