While the simulation protocol for equilibrating the polymer system appears consistent across different studies, various annealing methods have been employed in the MD simulations to obtain T g . Wu 8 implemented a step-wise cooling approach, where multiple NPT (constant number, pressure, and temperature) simulations were conducted from a higher to lower temperature range, allowing equilibration at each step. The study investigated different properties such as density, specific volume, mean square displacements of polymer systems, and various energy components to determine T g , revealing that density profiles offer consistent T g values for bulk or amorphous polymer systems. On the other hand, Klajmon et al. 9 utilized a continuous cooling method for annealing simulations of the polyethylene glycol (PEG) system, involving a single NPT simulation at a specific cooling rate. They observed a difference of 10-20 K in the effect of slow (5 K/ns) and fast (40 K/ns) cooling rates on T g . Also, they found that a hyperbolic fit reduces computational uncertainty compared to the conventional bilinear fit of density profiles. The list of reported T g based on MD simulations for the model PEO system is presented in Table . Besides the force field-dependence and different annealing methods mentioned already, other factors also contribute to the dispersion in the reported T g . It is known that the introduction of LiTFSI into the polymer system resulted in a 20-30 K increase in T g 10 due to the constraining effect on polymer segmental motion caused by the inclusion of salt, which forms physical cross-links between polymer chains. Further, the T g shows inversely depend on the polymer molecular weight as described by the Flory-Fox relation, , although MD simulations from Habasaki and Klajmon et al. do not show a clear trend in T g for systems with molecular weights higher than 1 kg/mol.