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About this Abstract
Meeting	MS&T21: Materials Science & Technology
Symposium	Accelerating Materials Science with Big Data and Machine Learning
Presentation Title	Bridging the Gap between Literature Data Extraction and Domain Specific Materials Informatics
Author(s)	Elsa Olivetti
On-Site Speaker (Planned)	Elsa Olivetti
Abstract Scope	Data has become a fundamental ingredient for accelerating and optimizing materials design and synthesis. Advances in applying natural language processing (NLP) to material science text has greatly increased the size and acquisition speed of materials science data from the published literature. This presentation will describe work to extract information from peer reviewed academic literature across a range of materials. Applying NLP pipelines to these types of materials science systems can be challenging due to the general schema and the noisiness of automatically extraction data. I will present data engineering techniques and discuss an optimal balance between automatic and manual data extraction.

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Bridging the Gap between Literature Data Extraction and Domain Specific Materials Informatics

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