Machine-learned and codified synthesis parameters of oxide materials
Author(s)
Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsa; Kim, Edward; Huang, Kevin Joon-Ming; Tomala, Alex; Matthews, Sara C.; ... Show more Show less
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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.
Date issued
2017-09Department
Massachusetts Institute of Technology. Department of Materials Science and EngineeringJournal
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
ISSN
2052-4463