  MD-GAN is a machine learning-based method that can evolve part of the system
at any time step, accelerating the generation of molecular dynamics data. For
the accurate prediction of MD-GAN, sufficient information on the dynamics of a
part of the system should be included with the training data. Therefore, the
selection of the part of the system is important for efficient learning. In a
previous study, only one particle (or vector) of each molecule was extracted as
part of the system. Therefore, we investigated the effectiveness of adding
information from other particles to the learning process. In the experiment of
the polyethylene system, when the dynamics of three particles of each molecule
were used, the diffusion was successfully predicted using one-third of the time
length of the training data, compared to the single-particle input.
Surprisingly, the unobserved transition of diffusion in the training data was
also predicted using this method.
