Silicon is the most widely used semiconductor material in electronic industry, including photovoltaics. In spite of this dominance and many decades of research, important questions remain unsolved.
Among them, the effect of grain boundaries on the electronic, thermal and optical properties of polycrystals. Unlike dopants and point defects, grain boundaries have remained underexplored with atomistic simulations.
The project's objective is to change this, using a combination of computational tools to address the coupled dynamics of electrons and phonons around the grain boundaries.
The team will take advantage of Machine Learning interatomic potentials to study phonon trapping at interfaces between crystal domains with different orientations, and to estimate the lifetimes of local vibrational modes via phonon-phonon coupling, or the thermal dependence of the Kapitza resistance.
On the other hand, the presence of in-gap states at the grains is critical for optoelectronic applications, and are also source of scattering (both for electrons and phonons).
The study of electron-phonon coupling interactions in these grain boundaries with DFT is computationally challenging, but here we will use a new computational framework that enables the study of hot carriers’s dynamics at the boundaries, and analyze thermalization channels to local vibrational modes and viceversa.