through Machine Learning and Density Functional Theory*

Lead-free metal halides, as emerging materials for photovoltaic and optoelectronic applications, have garnered substantial attention, especially given the increasing global emphasis on environmental sustainability. AgSbI₄ is an example of such a material that has recently been synthesized and shows intriguing characteristics but has not yet been adequately explored with density functional theory due to its pronounced site-disorder in its cation sublattice. We harness the potential of a kernel ridge regression 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 techniques. With these models, we calculate structural and optoelectronic properties ranging from X-ray diffraction patterns to absorption and reflection spectra. We compare these results with existing experimental data, for example, the average band gap of 1.96 eV, and lattice constants (a, c = 4.4, 21.0 Å). We calculate effective masses (〈m^*_e〉 = 0.4 m₀ and 〈m^*_h〉 = 4.1 m₀), bulk modulus (35 GPa), and formation energies with respect to AgI and SbI₃ (~50 meV per formula unit). We analyze the density of states, use LOBSTER to calculate Crystal Orbital Hamilton Populations, and perform Bader charge analysis to compute charge transfer. Significantly, our predictions concerning optoelectronic properties indicate AgSbI₄'s potential as an absorber layer in next-generation tandem photovoltaic cells.