Discovery of new materials has a documented history of propelling human
progress for centuries and more. The behaviour of a material is a function of
its composition, structure, and properties, which further depend on its
processing and testing conditions. Recent developments in deep learning and
natural language processing have enabled information extraction at scale from
published literature such as peer-reviewed publications, books, and patents.
However, this information is spread in multiple formats, such as tables, text,
and images, and with little or no uniformity in reporting style giving rise to
several machine learning challenges. Here, we discuss, quantify, and document
these outstanding challenges in automated information extraction (IE) from
materials science literature towards the creation of a large materials science
knowledge base. Specifically, we focus on IE from text and tables and outline
several challenges with examples. We hope the present work inspires researchers
to address the challenges in a coherent fashion, providing to fillip to IE for
the materials knowledge base.

材料科学文献信息提取中的机器学习挑战及建立材料知识库的启示。