NER is a subtask of an NLP pipeline that classifies keywords extracted from a given paragraph. This can be accomplished with the help of an NER tool. An NER tool trained on one set of data might not be useful for domain-specific terminology identification. Hence, different NER tools have been developed in the materials science domain to identify the chemicals, process keywords, units of measurements etc. Some of the commonly available NER tools are ChemicalTagger , OSCAR4 , Chemical Named Entities Recognition , and ChemDataExtractor . In this work, to extract all the chemicals (perovskites, solvents, etc.), we used ChemDataExtractor but to identify the solvents, we used NER tool developed by Kim et al. . Although this NER identified most of the solvents, there were a lot of false positives and false negatives as well. The solvents in our list that weren't identified by this NER are -Dichlorobenzene, 2-Methoxyethanol, Ethylenediamine, Ethanethiol, and 1-Methyl-2-pyrrolildinone (commonly known as the NMP solvent). Distribution of top 20 organic solvents used in perovskite synthesis