  Artificial intelligence in the form of deep learning is now very popular and
directly implemented in many areas of science and technology. In the present
work we study time evolution of Discrete Breathers in one-dimensional nonlinear
chains using the framework of Convolutional Neural Networks. We focus on
differentiating discrete breathers which are localized nonlinear modes from
linearized phonon modes. The breathers are localized in space and time-periodic
solutions of non-linear discrete lattices while phonons are the linear
collective oscillations of interacting atoms and molecules. We show that deep
learning neural networks are indeed able not only to distinguish breather from
phonon modes but also determine with high accuracy the underlying nonlinear
on-site potentials that generate breathers. This work can have extensions to
more complex natural systems.
