Bloch oscillations in graphene from an artificial neural network study

We develop an artificial neural network (ANN) approach to classify simulated signals corresponding to the semi-classical description of Bloch oscillations in pristine graphene. After the ANN is properly trained, we consider the inverse problem of Bloch oscillations (BO), namely, a new signal is classified according to the external electric field strength oriented along either the zig-zag or arm-chair edges of the graphene membrane, with a correct classification that ranges from 82.6% to 99.3% depending on the accuracy of the predicted electric field. This approach can be improved depending on the time spent in training the network and the computational power available. Findings in this work can be straightforwardly extended to a variety of Dirac-Weyl materials.