Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
Author(s)
Kim, Edward; Huang, Kevin Joon-Ming; Saunders, Adam; McCallum, Andrew; Ceder, Gerbrand; Olivetti, Elsa A.; ... Show more Show less
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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.
Date issued
2017-10Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering; Massachusetts Institute of Technology. Institute for Data, Systems, and SocietyJournal
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
ISSN
0897-4756
1520-5002