Machine Learning-Accelerated Band-Edge Engineering of Pnictogen Chalcohalide Solid Solutions for Solar Energy Technologies

Abstract

Pnictogen chalcohalide (MChX; M=Bi,Sb; Ch=S,Se; X=I,Br) solid solutions combine earth-abundant constituents, tunable band gaps ($1.2$-$2.1$ eV), and strong optical absorption, making them attractive for solar energy conversion. Yet their vast compositional space has so far prevented a systematic assessment of how band-edge positions vary with stoichiometry and surface termination. Here, we combine first-principles density functional theory with machine learning to predict the valence and conduction band-edge positions of $\mathrm{Bi}_x\mathrm{Sb}_{1-x}\mathrm{S}_y\mathrm{Se}_{1-y}\mathrm{I}_z\mathrm{Br}_{1-z}$ solid solutions across their full compositional range on the two most stable surfaces, (010) and (011). We find that the valence-band maximum and conduction-band minimum can be tuned by more than $1$ eV through composition alone, and shift by up to $0.6$ eV between the two surface terminations for a same composition despite their nearly degenerate formation energies, establishing facet selection as a design parameter on par with chemical substitution. Guided by these results, we identify specific compositions capable of driving hydrogen, ammonia, methane, hydrogen peroxide, and oxygen (photo)electrochemical half-reactions, and show that several electron- and hole-transport contact materials commonly used in photovoltaic devices align with MChX solid solutions only as hole-selective contacts.