Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning.

We introduce a machine learning prediction workflow to study the impact of defects on the Raman response of 2D materials. By combining the use of machine-learned interatomic potentials, the Raman-active $\Gamma$-weighted density of states method and splitting configurations in independant patches, we are able to reach simulation sizes in the tens of thousands of atoms, with diagonalization now being the main bottleneck of the simulation. We apply the method to two systems, isotopic graphene and defective hexagonal boron nitride, and compare our predicted Raman response to experimental results, with good agreement.

Efficient determination of Born-effective charges, LO-TO splitting, and Raman tensors of solids with a real-space atom-centered deep learning approach.

We introduce a deep neural network (DNN) framework called theeal-spacetomicecompositionwork (radnet), which is capable of making accurate predictions of polarization and of electronic dielectric permittivity tensors in solids and aims to address limitations of previously available machine learning models for Raman predictions in periodic systems. This framework builds on previous, atom-centered approaches while utilizing deep convolutional neural networks. We report excellent accuracies on direct predictions for two prototypical examples: GaAs and BN.

study of the processes of nitrogen functionalisation in graphene.

Nitrogen functionalisation of graphene is studied with the help ofelectronic structure methods. Both static formation energies and energy barriers obtained from nudged elastic band calculations are considered. If carbon defects are present in the graphene structure, low energy barriers on the order of 0.