Properties of AgBiI4 using high through-put DFT and machine learning methods

Silver iodo-bismuthates show promise for optoelectronic and other applications. Within this family of materials, AgBiI4 is a prominent model compound. The complexity has prevented conclusive determination specific atomic arrangements metal atoms in the bulk material. Here, we employ high through-put density functional novel machine learning methods to determine physically relevant unit cell configurations. We also calculate fundamental properties material using newly discovered Our results lattice constant (12.7 Å) bandgap (1.8 eV) agree with previous theory experiment. report new predictions modulus (7.5 GPa) temperature-dependent conductivity mass electrons (m0 at T = 300 K) holes (7m0 K); these masses will be useful AgBiI4-based device simulations.