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DTSTART:19700308T020000
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DTSTAMP:20190719T085745Z
LOCATION:HG E 1.2
DTSTART;TZID=Europe/Stockholm:20190614T110000
DTEND;TZID=Europe/Stockholm:20190614T113000
UID:submissions.pasc-conference.org_PASC19_sess127_msa313@linklings.com
SUMMARY:Extracting, Analyzing, and Planning Inorganic Materials Synthesis 
 using Literature-Trained Models
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Chemi
 stry and Materials\n\nExtracting, Analyzing, and Planning Inorganic Materi
 als Synthesis using Literature-Trained Models\n\nStrubell\n\nMany recent a
 dvances in materials design and discovery have been accelerated by data-dr
 iven methods that leverage structured datasets such as organic reaction da
 tabases. In stark contrast to organic synthesis, however, the vast majorit
 y of knowledge regarding inorganic synthesis remains unstructured, distrib
 uted across the natural language text of research articles; efficiently in
 tegrating this information is a bottleneck in inorganic materials synthesi
 s planning. In this talk I will describe how we use natural language proce
 ssing and machine learning to extract structured representations of materi
 als synthesis routes from the raw text of journal articles at large scale,
  and how we leverage these structured representations to inform materials 
 synthesis planning. I will conclude with a brief discussion of exciting fu
 ture directions for leveraging text for advanced materials discovery.
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