  As an important physical quantity to understand the internal structure of
polymer chains, the structure factor is being studied both in theory and
experiment. Theoretically, the structure factor of Gaussian chains have been
solved analytically, but for wormlike chains, numerical approaches are often
used, such as Monte Carlo (MC) simulations, solving modified diffusion equation
(MDE), etc. In those works, the structure factor needs to be calculated
differently for different regions of the wave vector and chain rigidity, and
some calculation processes are resource consuming. In this work, by training a
deep neural network (NN), we obtained an efficient model to calculate the
structure factor of polymer chains, without considering different regions of
wavenumber and chain rigidity. Furthermore, based on the trained neural network
model, we predicted the contour and Kuhn length of some polymer chains by using
scattering experimental data, and we found our model can get pretty reasonable
predictions. This work provides a method to obtain structure factor for polymer
chains, which is as good as previous, and with a more computationally
efficient. Also, it provides a potential way for the experimental researchers
to measure the contour and Kuhn length of polymer chains.
