Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning

Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning

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 transform...

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Bibliographic Details
Main Authors: Kim, Edward, Huang, Kevin Joon-Ming, Saunders, Adam, McCallum, Andrew, Ceder, Gerbrand, Olivetti, Elsa A.
Other Authors: Massachusetts Institute of Technology. Department of Materials Science and Engineering
Format: Article
Language:English
Published: American Chemical Society (ACS) 2021
Online Access:https://hdl.handle.net/1721.1/129530

Internet

https://hdl.handle.net/1721.1/129530

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