期刊
SCIENTIFIC DATA
卷 4, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/sdata.2017.127
关键词
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资金
- National Science Foundation [1534340]
- Office of Naval Research (ONR) [N00014-16-1-2432]
- MIT Energy Initiative
- NSERC
- Division Of Materials Research
- Direct For Mathematical & Physical Scien [1534340] Funding Source: National Science Foundation
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. [GRAPHICS] .
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