Our overall work can be summarized in two main points. First, we have demonstrated the value of contextual NLP to identify the key organic solvents used in the context of the different stages of synthesizing perovskite devices. From this selection of solvents, a machine learning model integrated with an uncertainty metric, was applied to screen out potential endocrine disruptors. This allows a better estimation of the result quality, thereby making our approach helpful in the face of data scarcity. Hence, we expect this work to be influential at least in screening out the potential endocrine disrupting chemicals, especially when sufficient data on them isn't available.