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|a Strubell, Emma
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|a Kim, Edward
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|a Huang, Kevin Joon-Ming
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|a Tomala, Alex
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|a Matthews, Sara C.
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|a Saunders, Adam
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|a McCallum, Andrew
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|a Olivetti, Elsa
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|a Kim, Edward
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|a Huang, Kevin Joon-Ming
|e author
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|a Tomala, Alex
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|a Matthews, Sara C.
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|a Machine-learned and codified synthesis parameters of oxide materials
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|b Nature Publishing Group,
|c 2018-06-15T15:53:35Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/116340
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|a 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.
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|a Article
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|t Scientific Data
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