Computer simulation and machine learning of polymer collapse and critical adsorption phase transitions

Collapse and critical adsorption of polymers are two crucial phase transitions in polymer science, both are accompanied by significant changes in polymer conformation. In this paper, Langevin dynamics and dynamic Monte Carlo methods are used to simulate the collapse and critical adsorption of polymer, respectively, and corresponding phase transition temperatures are estimated. Meanwhile, a large number of polymer conformations at different temperatures are obtained. In the machine learning method, a large number of extended random coil and collapsed spherical, desorption and adsorption conformations are used to train the neural network, so that the neural network can learn the characteristics of different states of the polymer, and it can quickly and accurately analyze the polymer conformations at different temperatures and obtain the corresponding collapse phase transition temperature and critical adsorption temperature. The results demonstrate that machine learning can correctly calculate the phase transition temperature of polymer system, which provides new ideas and methods for machine learning technology in the study of polymer phase transitions.