Investigation of optoelectronic properties of AgSbI4 using machine learning and first principles methods

Abstract

Lead-free metal halides have potential uses in photovoltaic and other optoelectronic applications. AgSbI4 is an example of a lead-free metal halide that has recently been synthesized but not yet fully treated with theoretical calculations due to the large degree of site disorder in its cation sublattice. We employ a machine learning model of the total energy in AgSbI4 to choose a few simulation cells out of ∼107 possibilities; a task out of reach for current first principles methods. We calculate the structural and optoelectronic properties such as X-ray diffraction patterns and band gaps of these cells and find that they agree well with existing experimental data. Additionally, new predictions of the optoelectronic properties indicate that AgSbI4 may be a good choice for use as an absorber layer in tandem photovoltaic cells.