While imputing data can be a valuable technique to enhance the quality and completeness of a dataset, it can also be unproductive (no change in model performance) or even counterproductive (degraded model performance). If the proportion of missing data is large (30-40%), imputing may lead to inaccurate results. It may also be counterproductive in instances where there is a pattern to which data is missing, or where missing data is associated with specific characteristics or factors because the ML model can potentially learn from such correlations. Finally, imputation may be challenging in datasets with intricate dependencies between variables, as is likely the case for OPV materials and devices. Ultimately, imputing data did not change model performance in any measurable way (Figure ). This suggests that the models are limited not only by the quantity of data, but also by their quality.