Given the many complex relationships and processes that ultimately determine OPV device performance, the potential of machine learning (ML) to accelerate the development of OPV materials and devices has emerged as a tantalizing promise. ML techniques could allow scientists to quickly screen potential combinations of donor (D) and acceptor (A) materials, or suggest the most appropriate device fabrication parameters for a given combination of materials. Previous efforts in using ML to predict the PCE of OPV devices have been focused exclusively on the donor and acceptor materials. Notably, the Harvard Clean Energy Project and others have used DFT-computed molecular descriptors to predict PCEs. A similar approach has recently incorporated genetic algorithms in an effort to design high-performing non-fullerene acceptorbased (NFA) devices. Others have generated one-hot encodings based on human intuition regarding molecular substructures. However, the most accurate models to date are those that have used high-throughput domain-specific descriptors. Notably, none of the aforementioned works incorporate information regarding the device fabrication parameters, whichas discussed aboveare known to play an important role in determining PCE.