• Corpus ID: 31322594

Synthesis Insights from Scienti fi c Literature via Text Extraction and Machine Learning

@inproceedings{Kim2017SynthesisIF,
  title={Synthesis Insights from Scienti fi c Literature via Text Extraction and Machine Learning},
  author={Edward Kim and Kevin Huang and Adam Saunders and Andrew McCallum and Gerbrand Ceder and Elsa A. Olivetti},
  year={2017}
}
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… 

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