文章基本信息

标题：Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature
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作者：Zheren Wang ; Olga Kononova ; Kevin Cruse 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-11
DOI：10.1038/s41597-022-01317-2
语种：English
出版社：Nature Publishing Group
摘要：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 procedures extracted from the scientifc literature . Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes . Every procedure is also augmented with the reaction formula . Through this work, we are making freely available the frst large dataset of solution-based inorganic materials synthesis procedures.