The rapid development and application of machine learning (ML) techniques in materials science have led to new tools for machine-enabled and autonomous/high-throughput materials design and discovery. Alongside, efforts to extract data from traditional experiments in the published literature with natural language processing (NLP) algorithms provide opportunities to develop tremendous data troves for these in silico design and discovery endeavors. While NLP is used in all aspects of society, its application in materials science is still in the very early stages. This perspective provides a case study on the application of NLP to extract information related to the preparation of organic materials. We present the case study at a basic level with the aim to discuss these technologies and processes with researchers from diverse scientific backgrounds. We also discuss the challenges faced in the case study and provide an assessment to improve the accuracy of NLP techniques for materials science with the aid of community contributions.
|Number of pages||7|
|Journal||Chemistry of Materials|
|State||Published - Jun 14 2022|
Bibliographical noteFunding Information:
This work was sponsored by the National Science Foundation in part through the Designing Materials to Revolutionize and Engineer our Future (NSF DMREF) program under Award Number DMR 1627428 and the Established Program to Stimulate Competitive Research (EPSCoR) Track 2 program under Cooperative Agreement Number 2019574. We acknowledge the University of Kentucky Center for Computational Sciences and Information Technology Services Research Computing for their fantastic support and collaboration and use of the Lipscomb Compute Cluster and associated research computing resources.
© 2022 American Chemical Society.
ASJC Scopus subject areas
- Chemistry (all)
- Chemical Engineering (all)
- Materials Chemistry