  In this paper, we present a novel approach to knowledge extraction and
retrieval using Natural Language Processing (NLP) techniques for material
science. Our goal is to automatically mine structured knowledge from millions
of research articles in the field of polycrystalline materials and make it
easily accessible to the broader community. The proposed method leverages NLP
techniques such as entity recognition and document classification to extract
relevant information and build an extensive knowledge base, from a collection
of 9.5 Million publications. The resulting knowledge base is integrated into a
search engine, which enables users to search for information about specific
materials, properties, and experiments with greater precision than traditional
search engines like Google. We hope our results can enable material scientists
quickly locate desired experimental procedures, compare their differences, and
even inspire them to design new experiments. Our website will be available at
Github \footnote{https://github.com/Xianjun-Yang/PcMSP.git} soon.
