Our findings underscore the challenges stemming from mining literature data and inherent data quality issues. Although recent advances in literature mining with large language models are promising, the ultimate problem is one of data quality and not quantity. Standardizing data reporting in OPV-related publications becomes crucial. Establishing minimum reporting standards will streamline literature mining and enable the adoption of ML methodologies within the OPV domain. There has long been discussion in the literature about standards for reporting PCE measurements. While there are currently no standards or requirements for reporting how OPV devices are made, similar steps forward have recently been proposed for inorganic phosphors and perovskite photovoltaics. There is much that can be learned from the ongoing discussions within the organic chemistry literature by recognizing the similarities between predicting OPV device performance and predicting reaction yields in organic chemistry sheds light on shared complexities. Both domains face challenges involving two molecular structures as input features, processing variables, sparse data, non-smooth response surfaces, and biases in reported data, notably the underreporting of negative results. Acknowledging these parallels emphasizes the broader challenges in predictive modeling within chemistry-driven domains. Addressing these challenges necessitates enhanced dataset quality and size, and standardized data reporting. This comprehensive understanding highlights the pivotal role of data quality, underscores the importance of standardized reporting practices, and emphasizes the collective effort required to enhance dataset quality and modeling techniques, ultimately unlocking the full potential of ML in driving innovation within the OPV and broader chemistry domains.