Machine-learned and codified synthesis parameters of oxide materials
Name
sdata2017127.pdf
Size
1 MB
Format
Adobe PDF
Checksum (MD5)
9503150eb632ecccd9c7082a1891c167
Author(s)
Strubell, Emma
Saunders, Adam
McCallum, Andrew
Olivetti, Elsa
Kim, Edward
Huang, Kevin Joon-Ming
Tomala, Alex
Matthews, Sara C.
Date Issued
September 2017
Journal
Scientific Data
Publisher
Nature Publishing Group
Citation
Kim, Edward et al. “Machine-Learned and Codified Synthesis Parameters of Oxide Materials.” Scientific Data 4 (September 2017): 170127
Version
Final published version
Abstract
Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Attribution 4.0 International (CC BY 4.0)
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/SDATA.2017.127
