Organometallic complexes as donors and non-fullerene acceptors for organic solar cells: DFT-guided design,...

Computational screening has become indispensable to organic solar cell (OSC) design, allowing the electronic, optical, and charge-transport properties of a candidate to be predicted before synthesis. Organometallic complexes (OMCs) are especially well suited to this approach: their metal centers offer design handles-tunable frontier-orbital energies, strong spin-orbit coupling (SOC), and metal-to-ligand charge-transfer (MLCT) transitions that purely organic semiconductors lack. Long confined to donor and interlayer roles, OMCs have recently emerged as credible non-fullerene acceptors (NFAs) once functionalized with electron-withdrawing groups, yet their donor and acceptor chemistries have been reviewed in isolation rather than within a common density functional theory (DFT) framework. This review examines how DFT and its time-dependent extension (TD-DFT) guide the design of OMCs that function as both donors and NFAs. We assess the prevailing computational protocols-hybrid and range-separated functionals (B3LYP, CAM-B3LYP, ωB97XD, M06-2X), effective core potentials (LANL2DZ), and continuum solvation models (PCM, SMD) and the descriptors that link molecular structure to device performance: frontier molecular orbital (FMO) energies, reorganization and exciton-binding energies, and SOC matrix elements. The literature is organized by metal center-ferrocene (Fe), iridium (Ir), platinum (Pt), ruthenium (Ru), osmium (Os), and metalloporphyrin/phthalocyanine systems. Across the surveyed systems, DFT-computed band gaps reproduce experimental trends well, and an optimal acceptor LUMO window of -3.5 to -3.9 eV emerges. Within this window, we rationalize the record power conversion efficiencies of 19.24% (ternary Pt-based) and 15.25% (ternary Cu-porphyrin) devices. We distill these findings into transferable, metal-specific design rules alongside a candid assessment of method reliability. We close by charting the integration of machine learning with high-throughput DFT toward OMC-NFAs that exceed 20% efficiency with improved operational stability.