During the training phase, BERT uses a Masked Language Model (MLM) that masks randomly selected words in an input paragraph and uses a neural network to predict the missing word. It adds a classification layer on the top of the encoder output and computes the probability of all possible words that can fill the randomly selected gaps. The pre-trained BERT is trained on the SQuAD dataset that has 100,000+ question and answers pairs based on 500+ articles. Our database of around 30,000 articles acts as the input corpus based on which CDQA provides relevant answers. The question asked was -"How are perovskites synthesized?". As shown in Figure , on an average, we extracted three answers and the corresponding paragraphs where those answers are based. It is not necessary that all the articles we downloaded contain information on perovskite synthesis. The QA system filtered a large chunk of the irrelevant articles and provided around 30,000 useful paragraphs. From these paragraphs, we identified the keywords that are vital to perovskite synthesis.