The term stopwords usually refers to words of low importance to the NLP task and which usually occur very often. Since you’re saying that the words you want removed are domain-related I’m going to assume that they do not contribute any meaningful information for your machine learning model.
A simple way to identify these words would be to look at the most frequently occurring words and see if the possible stopwords are there. Your could check the words by looking for high term frequency but in this case looking at low inverse document frequency (the denominator term from TF-IDF) would be more relevant since it would reveal the most frequent words across multiple documents.
You will still have to go through the sorted list of words and manually decide whether to place a word in the stopwords pile since these are domain-specific terms and a preexisting list (like the one in scikit-learn) probably does not exist. Your best bet for such a list would be to consult domain experts to get a head start on creating such a list.