Machine-Learning Provides New Insights into the Coil-to-Globule Transitions of Thermosensitive Polymers

First of its kind, a temperature-independent coarse-grained (CG) model of poly(N-isopropylacrylamide) (PNIPAM) that can accurately predict its experimental lower critical solution temperature (LCST) in the presence of explicit water model is developed. This extensively validated CG model by conducting MD simulations by changing the radius of gyration of initial structure, the chain length, and the angle between the adjacent monomers of the initial configuration of PNIPAM. The model could retain PNIPAM’s tacticity and thereby predict its LCST, which is consistent with experiments and all-atom simulations. A data-driven machine-learning (ML) approach, non-metric multidimensional scaling (NMDS) method, was used to analyze these CG MD simulation trajectories. This analysis suggest that PNIPAM chain undergoes a coil-to-globule transition above the LCST via multiple metastable states.