Text-mined dataset of inorganic materials synthesis recipes

Kononova, Olga; Huo, Haoyan; He, Tanjin; Rong, Ziqin; Botari, Tiago; Sun, Wenhao; Tshitoyan, Vahe; Ceder, Gerbrand

doi:10.1038/s41597-019-0224-1

Title: Text-mined dataset of inorganic materials synthesis recipes

Abstract

Abstract Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of “codified recipes” for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.

Authors:

;

; He, Tanjin; Rong, Ziqin; Botari, Tiago;

; Tshitoyan, Vahe; Ceder, Gerbrand

Publication Date:: Tue Oct 15 00:00:00 EDT 2019

Research Org.:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); National Science Foundation (NSF)

OSTI Identifier:: 1619609

Alternate Identifier(s):: OSTI ID: 1580948

Grant/Contract Number:: AC02-05CH11231; N00014-14-1-0444; 1534340

Resource Type:: Published Article

Journal Name:: Scientific Data

Additional Journal Information:: Journal Name: Scientific Data Journal Volume: 6 Journal Issue: 1; Journal ID: ISSN 2052-4463

Publisher:: Nature Publishing Group

Country of Publication:: United Kingdom

Language:: English

Subject:: 96 KNOWLEDGE MANAGEMENT AND PRESERVATION

Citation Formats


                    Kononova, Olga, Huo, Haoyan, He, Tanjin, Rong, Ziqin, Botari, Tiago, Sun, Wenhao, Tshitoyan, Vahe, and Ceder, Gerbrand. Text-mined dataset of inorganic materials synthesis recipes.  United Kingdom: N. p., 2019. 
Web.  doi:10.1038/s41597-019-0224-1.

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                    Kononova, Olga, Huo, Haoyan, He, Tanjin, Rong, Ziqin, Botari, Tiago, Sun, Wenhao, Tshitoyan, Vahe, & Ceder, Gerbrand. Text-mined dataset of inorganic materials synthesis recipes.  United Kingdom.  https://doi.org/10.1038/s41597-019-0224-1

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                    Kononova, Olga, Huo, Haoyan, He, Tanjin, Rong, Ziqin, Botari, Tiago, Sun, Wenhao, Tshitoyan, Vahe, and Ceder, Gerbrand. Tue .  
"Text-mined dataset of inorganic materials synthesis recipes".  United Kingdom.  https://doi.org/10.1038/s41597-019-0224-1.

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@article{osti_1619609,

  title        = {Text-mined dataset of inorganic materials synthesis recipes},

  author       = {Kononova, Olga and Huo, Haoyan and He, Tanjin and Rong, Ziqin and Botari, Tiago and Sun, Wenhao and Tshitoyan, Vahe and Ceder, Gerbrand},

  abstractNote = {Abstract Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of “codified recipes” for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.},

  doi          = {10.1038/s41597-019-0224-1},

  journal      = {Scientific Data},

  number       = 1,

  volume       = 6,

  place        = {United Kingdom},

  year         = {Tue Oct 15 00:00:00 EDT 2019},

  month        = {Tue Oct 15 00:00:00 EDT 2019}

}

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Works referenced in this record:

Information Retrieval and Text Mining Technologies for Chemistry
journal, May 2017

Krallinger, Martin; Rabal, Obdulia; Lourenço, Anália
Chemical Reviews, Vol. 117, Issue 12
DOI: 10.1021/acs.chemrev.6b00851

ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature
journal, October 2016

Swain, Matthew C.; Cole, Jacqueline M.
Journal of Chemical Information and Modeling, Vol. 56, Issue 10
DOI: 10.1021/acs.jcim.6b00207

Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access
journal, June 2016

O’Mara, Jordan; Meredig, Bryce; Michel, Kyle
JOM, Vol. 68, Issue 8
DOI: 10.1007/s11837-016-1984-0

From DFT to machine learning: recent approaches to materials science–a review
journal, May 2019

Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera
Journal of Physics: Materials, Vol. 2, Issue 3
DOI: 10.1088/2515-7639/ab084b