ULSA: unified language of synthesis actions for the representation of inorganic synthesis protocols
ULSA: unified language of synthesis actions for the representation of inorganic synthesis protocols
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ULSA：用于表示无机合成方案的合成操作的统一语言
DOI：
10.1039/d1dd00034a
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Ceder, Gerbrand
中科院分区：
文献类型：
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作者：
Wang, Zheren;Cruse, Kevin;Fei, Yuxing;Chia, Ann;Zeng, Yan;Huo, Haoyan;He, Tanjin;Deng, Bowen;Kononova, Olga;Ceder, Gerbrand
应用人工智能预测新材料的合成需要高质量的大规模数据集。从科学出版物中提取合成信息仍然具有挑战性，特别是对于提取合成操作，因为缺乏使用可靠，稳健和完善的本体来描述合成程序的综合标记数据集。在这项工作中，我们提出了第一个统一的语言合成行动（ULSA）描述无机合成程序。我们创建了一个数据集的3040合成程序的领域专家根据建议的ULSA计划进行注释。为了证明ULSA的能力，我们建立了一个基于神经网络的模型，将任意无机合成段落映射到ULSA中，并使用它来构建合成流程图。对流程图的分析表明：（a）ULSA涵盖了研究人员在描述合成程序时使用的基本词汇;（B）它可以捕获合成方案的重要特征。目前的工作集中在固态，溶胶-凝胶和溶液为基础的无机合成的合成协议，但语言可以扩展到包括其他合成方法在未来。这项工作是一个重要的一步，创造一个合成本体和自主机器人合成的坚实基础。
Applying AI power to predict syntheses of novel materials requires high-quality, large-scale datasets. Extraction of synthesis information from scientific publications is still challenging, especially for extracting synthesis actions, because of the lack of a comprehensive labeled dataset using a solid, robust, and well-established ontology for describing synthesis procedures. In this work, we propose the first unified language of synthesis actions (ULSA) for describing inorganic synthesis procedures. We created a dataset of 3040 synthesis procedures annotated by domain experts according to the proposed ULSA scheme. To demonstrate the capabilities of ULSA, we built a neural network-based model to map arbitrary inorganic synthesis paragraphs into ULSA and used it to construct synthesis flowcharts for synthesis procedures. Analysis of the flowcharts showed that (a) ULSA covers essential vocabulary used by researchers when describing synthesis procedures and (b) it can capture important features of synthesis protocols. The present work focuses on the synthesis protocols for solid-state, sol–gel, and solution-based inorganic synthesis, but the language could be extended in the future to include other synthesis methods. This work is an important step towards creating a synthesis ontology and a solid foundation for autonomous robotic synthesis.