Information on the usage of solvents for perovskites is complex and details vary from material to material . There is abundant literature both on synthesis of perovskite solar cells and the usage of solvents in such synthesis . Unfortunately, there is no comprehensive dataset that possesses the intersection of those information. Although there have been studies to gather the solvents, assess their hazardous nature and rank them via different metrics , these are not in conjunction with perovskite synthesis and the endocrine disrupting (ED) nature of the solvents. Studies on perovskite synthesis mention only a few solvents (usually around 5) with Dimethylformamide (DMF), and Dimethylsulfoxide (DMSO) virtually appearing in almost all the articles . However, scouring the literature reveals that numerous other solvents are used during perovskite synthesis apart from the "more popular" ones. Solvents such as Toluene, Isopropanol and Chlorobenzene are anti-solvents, and are used in washing/rinsing the previously used solvents to get precursor precipitates while Oleylamine (OLA) and Oleic acid (OA) are found being used in post-synthetic treatments of solid-state fabrication . Hence, it can be inferred that different solvents have different roles to play in different stages of perovskites synthesis . But this information hasn't been organized and it isn't easy to do so. The challenges on information extraction from the literature has been well documented in the article by Hong et al. . The ever-increasing number of scientific articles mean that different solvents and synthesis techniques are being discussed and it is difficult to keep track of them manually. The manual approach to mining data from the literature is not pragmatic, given the vast amount of information and the arbitrary nature of the data . Significant amount of scientific information is published as text, which is not feasible to analyze by traditional statistical methods. This challenge is addressed by the NLP and machine learning (ML) tools that can be automated to read and extract information from a large number of documents . NLP facilitates generation of structured database which can be queried upon by using computational tools to boost data-driven projects for materials discovery .