Auto-generated Materials Database of Curie and Néel Temperatures via Semi-supervised Relationship Extraction

Published in Nature Scientific Data, 2018

Recommended citation: Court C.J & Cole J.M "Auto-generated Materials Database of Curie and Néel Temperatures via Semi-supervised Relationship Extraction" Scientific Data. 5, 180111 (2018) https://www.nature.com/articles/sdata2018111

Description

This work develops a probabilistic approach to quaternary chemical relationship extraction from scientific text. Largely based on extensions to the Snowball Algorithm.

The tool was used to create a vast database of magnetic phase transition temperatures - the first auto-generated database of its kind.

The resulting database was later used by others to discover novel magnetocaloric effects in $HoB_2$. You can read that work here