Data-mining natural language materials syntheses

Discovering, designing, and developing a novel material is an arduous task, involving countless hours of human effort and ingenuity. While some aspects of this process have been vastly accelerated by the advent of first-principles-based computational techniques and high throughput experimental methods, a vast ocean of untapped historical knowledge lies dormant in the scientific literature. Namely, the precise methods by which many inorganic compounds are synthesized are recorded only as text within journal articles. This thesis aims to realize the potential of this data for informing the syntheses of inorganic materials through the use of data-mining algorithms. Critically, the methods used and produced in this thesis are fully automated, thus maximizing the impact for accelerated synthesis planning by human researchers.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Materials Science and Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Kim, Edward Soo.

Advisor dc:contributor.advisor

• Elsa Olivetti.

• Materials Science and Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122075

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122075