Predicting the density of states of crystalline materials via machine learning

Spectral properties, such as the density of states, which are central for understanding the fundamental properties of materials, have been so far accessed via experiments and ab initio computations. Nowadays, machine learning methods have been applied in computational materials science enabling accelerated discovery mainly via scalar properties predictions, such as the electronic band gap. However, the application of these methods on predicting spectral properties of crystalline compounds is still in its infancy. In this context, we present an overview of the recent materials-to-spectrum (Mat2Spec) model, which outperforms state-of-the-art methods in predicting the ab initio phonon and electronic density of states of crystalline compounds, combining different machine learning techniques. As a proof of concept, we apply this model to identify new materials with gaps below the Fermi energy in the electronic density of states, which are pertinent to thermoelectrics and transparent conductors, and validate the predictions with DFT calculations. Finally, further developments of the model will be discussed.