Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
Name
2017_kim_paper_database.pdf
Description
Accepted version
Size
2.45 MB
Format
Adobe PDF
Checksum (MD5)
45cee70b7e12fdcffa9a31ac88639f49
Author(s)
Kim, Edward
Huang, Kevin Joon-Ming
Saunders, Adam
McCallum, Andrew
Ceder, Gerbrand
Olivetti, Elsa A.
Date Issued
October 2017
Journal
Chemistry of Materials
Publisher
American Chemical Society (ACS)
Citation
Kim, Edward et al. "Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning." Chemistry of Materials 29, 21 (October 2017): 9436–9444 © 2017 American Chemical Society
Version
Author's final manuscript
Abstract
In the past several years, Materials Genome Initiative (MGI) efforts have produced myriad examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage as well as large data resources that are used to screen for potentially transformative compounds. The bottleneck in high-Throughput materials design has thus shifted to materials synthesis, which motivates our development of a methodology to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using natural language processing techniques. To demonstrate our framework's capabilities, we examine the synthesis conditions for various metal oxides across more than 12 thousand manuscripts. We then apply machine learning methods to predict the critical parameters needed to synthesize titania nanotubes via hydrothermal methods and verify this result against known mechanisms. Finally, we demonstrate the capacity for transfer learning by using machine learning models to predict synthesis outcomes on materials systems not included in the training set and thereby outperform heuristic strategies.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acs.chemmater.7b03500
