Machine-learned and codified synthesis parameters of oxide materials

• Main Authors: Strubell, Emma (Author), Saunders, Adam (Author), McCallum, Andrew (Author), Olivetti, Elsa (Author), Kim, Edward (Contributor), Huang, Kevin Joon-Ming (Contributor), Tomala, Alex (Contributor), Matthews, Sara C. (Contributor)
• Format: Article
• Language: English
• Published: Nature Publishing Group, 2018-06-15T15:53:35Z.
• Subjects: Article
• Online Access: Get fulltext

 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.