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.
@article{arxiv.2302.05597,
title = {MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures},
author = {Xianjun Yang and Stephen Wilson and Linda Petzold},
journal= {arXiv preprint arXiv:2302.05597},
year = {2023}
}