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• W2755202310 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." @default.
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• W2755202310 date "2017-09-12" @default.
• W2755202310 modified "2023-10-17" @default.
• W2755202310 title "Machine-learned and codified synthesis parameters of oxide materials" @default.
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• W2755202310 doi "https://doi.org/10.1038/sdata.2017.127" @default.
• W2755202310 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5595045" @default.
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