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• W3215319246 abstract "The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis recipes extracted from the scientific literature. Each recipe contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every recipe is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis recipes." @default.
• W3215319246 created "2021-12-06" @default.
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• W3215319246 date "2021-11-21" @default.
• W3215319246 modified "2023-10-10" @default.
• W3215319246 title "Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature" @default.
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• W3215319246 doi "https://doi.org/10.48550/arxiv.2111.10874" @default.
• W3215319246 hasPublicationYear "2021" @default.
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