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Machine-learned and codified synthesis parameters of oxide materials.

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.

SUBMITTER: Kim E

PROVIDER: S-EPMC5595045 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Machine-learned and codified synthesis parameters of oxide materials.

Kim Edward E Huang Kevin K Tomala Alex A Matthews Sara S Strubell Emma E Saunders Adam A McCallum Andrew A Olivetti Elsa E

Scientific data 20170912

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 whi ...[more]

PMID: 28895943

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