  In this work we approach the Schr\"odinger equation in quantum wells with
arbitrary potentials, using the machine learning technique. Two neural networks
with different architectures are proposed and trained using a set of
potentials, energies, and wave functions previously generated with the
classical finite element method. Three accuracy indicators have been proposed
for testing the estimates given by the neural networks. The networks are
trained by the gradient descent method and the training validation is done with
respect to a large training data set. The two networks are then tested for two
different potential data sets and the results are compared. Several cases with
analytical potential have also been solved.

Computational Physics;Data Analysis, Statistics and Probability