Data-driven materials research enabled by natural language processing and information extraction
Abstract
Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains—enabled by techniques adapted from the field of natural language processing—therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. We discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2020
- Source ID
- 10.1063/5.0021106
Entities
People
- Anna M Hiszpanski
- Edward Kim
- Elsa A Olivetti
- Gerbrand Ceder
- Jacqueline M. Cole
- Olga Kononova
- Thomas Yong-Jin Han
Organizations
- ISIS Neutron and Muon Source
- Lawrence Berkeley National Laboratory
- Lawrence Livermore National Laboratory
- Massachusetts Institute of Technology
- National Science Foundation
- Office of Naval Research
- Royal Academy of Engineering
- Science and Technology Facilities Council
- United States Department of Energy
- University of Cambridge