January 1, 2019
2 MIN READ
Deep Learning for Advanced Materials
A team of ORNL researchers has developed a series of deep learning models allowing rapid access to the atomic degrees of freedom in noisy static and dynamic scanning transmission electron microscopy (STEM) data, access which not possible with existing methods. The models were successful in detecting structure symmetry breaking in graphene and multiferroic systems.