Scientific Question Answering (SQA) is a challenging open-domain task which requires the capability to understand questions and choices, collect useful information, and reason over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build 1) an essential-term-aware ‘retriever’ which first identifies the most important words in a question, then reformulates the queries and searches for related evidence 2) an enhanced ‘reader’ to distinguish between essential terms and distracting words to predict the answer. We experimentally evaluate our model on the ARC dataset where it outperforms the existing state-of-the-art model by 7.4%.