Semantic Scholar was built by the Allen Institute for AI to help researchers cut through information overload. Instead of matching keywords, it uses machine learning to understand what a paper is actually about, then surfaces the most relevant work. It pulls out key phrases, identifies figures and tables, and shows citation context so you can see how a paper has been used. The library covers over 200 million papers across all scientific fields.

You can follow authors, save papers to a library, and get recommendations based on your reading history. The platform is free and has an API for developers who want to build on the data. For someone entering a new research area, it's one of the fastest ways to find the papers that matter without reading through dozens of irrelevant results first.