Figure gives a pictorial summary of the work that we have done for this paper. In this work, we utilize contextual NLP to go through thousands of journal articles and extract texts on perovskite synthesis. This data is further processed through named entity recognition tools to identify the solvents and the perovskites. The Simplified Molecular-Input Line-Entry System (SMILES) codes of these solvents are obtained and processed through a deep learning model to assess their chemical hazard. In section 1, we discuss the complexity with which the information on such solvents is distributed in the literature and the usage of NLP tools to generate a structured database of these solvents. Our approach to acquiring the articles is discussed in section 2.1 while NLP implementation is elaborated in sections 2.2 and 2.3. The distribution of the solvents, their usage in synthesis of different perovskites, and their classification as EDCs are further detailed in section 3. The ambiguity associated with the deep-learning model is discussed and quantified in section 4. In section 5, we briefly discuss some of the references that back up our data and a few takeaways from this work. We provide our conclusion in section 6 and a listing of the supplementary materials in section 7. The articles are mainly downloaded by using an API, which are then processed using contextual NLP to extract answers to the given question. These answers and the paragraphs containing them are processed using named entity recognition (NER) tools to extract chemicals and synthesis keywords. ChemDataExtractor was used to extract all the chemicals while another NER was used to identify solvents and process keywords within the extracted texts. This information forms a dataset that can be used to create different data-driven models catering to various scientific questions. The solvent SMILES were fed into a deep-learning model to identify if these are endocrine disrupting chemicals. Finally, an uncertainty quantification of the classification is done to capture the model error.