Photovoltaics and Solar Energy

Solar cell efficiency has approached theoretical limits for established absorber materials like crystalline silicon and GaAs, yet next-generation photovoltaic technologies — perovskites, organic-inorganic hybrids, multi-junction tandems — face a generation challenge: the space of possible absorber compositions, contact layers, and heterostructure configurations is enormous, while stability and manufacturability constraints eliminate most candidates. Perovskite solar cells exemplify this tension — thousands of possible A-site, B-site, and halide combinations exist, but only a tiny fraction combine high efficiency with the operational stability required for commercial deployment. The field needs systematic generation of candidates that satisfy efficiency, stability, and processability constraints simultaneously, not sequential optimization of each property independently.

Current photovoltaic material discovery relies on Shockley-Queisser analysis to identify optimal bandgaps, followed by database screening or combinatorial synthesis to find materials near those targets. This approach treats the absorber in isolation from the full device stack, ignoring interface recombination, contact resistance, and encapsulation compatibility that determine real-world performance. Machine learning models trained on reported efficiencies inherit the biases of historical research — overrepresenting silicon and a few perovskite compositions while providing no reliable predictions for novel chemistries. Compositional screening of perovskite variants generates candidates but cannot enforce the stability constraints that eliminate most compositions from practical consideration.