Difference between revisions of "Journal:Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database"
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==Introduction== | ==Introduction== | ||
[[Materials informatics]] is the study of the data-oriented understanding of [[materials science]] data, represented by structures, properties, mechanisms, and protocols. [1] [[Artificial intelligence]] (AI) has been used in the field for automated material design, massive [[Data analysis|data analyses]], and accelerated experiments with robots to advance the discovery of materials for energy- and environment-related applications. [1,2,3,4,5] | |||
A long-term challenge in materials informatics and materials science is lossless [[data sharing]] by the scientific community. [6] Although materials and devices are sensitive to their preparation processes, materials databases and scientific documents generally do not provide sufficient [[information]]. [1,7,8] Most databases focus on structure–property relations and ignore or shorten the preparation protocols. [1,4,6,8] Experimental methods are available in scientific journals, but only specialists can appropriately extract the structure–property–process relationships from the text, and automated text parsing by AI is not yet practical. [7,9] Furthermore, detailed information—including non-representative experimental protocols, lot numbers of reagents, and raw measurement data—is often omitted from articles, which leaves major uncertainties about a material's data. As such, researchers may need to improve their communication style to achieve lossless material data sharing. | |||
Given these factors, we propose a data platform that can explicitly describe the relations among the structures, properties, and processes of materials (Fig. 1). Based on the concepts of knowledge graphs or flowcharts [7,10], all experimental events are connected as nodes in graphs. Most experimental information can be described losslessly as graphs, the format of which is also compatible with data science. [7] We demonstrated the system by using it in our research of [[wikipedia:Fast ion conductor#Superionic conductors|superionic organic conductors]], which revealed the factors for achieving a remarkable room-temperature conductivity of 10<sup>−4</sup> to 10<sup>−3</sup> S/cm and a Li<sup>+</sup> transference number of 0.8, practically the highest values of known tested organic solid-state conductors without plasticizers. [11,12,13,14,15] All experimental data, including everyday experimental operations and measurements (over 500 records), were recorded in the database and are available from a public repository. This work is ultimately representative of the demonstration in experimental materials science of the everything-open research style, which should become the standard for scientific communication to accelerate the integration of materials knowledge. | |||
[[File:Fig1 Hatakeyama-Sato njpCompMat22 8.png|800px]] | |||
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| style="background-color:white; padding-left:10px; padding-right:10px;"| <blockquote>'''Figure 1.''' Graph-shaped material data storage system. All experimental results were recorded as graph-shaped data and automatically converted into a table database for analysis (see Supplementary Fig. 1 for a representative case). Missing values were imputed by machine learning.</blockquote> | |||
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==Results== | |||
===Recording daily experiments as graph-shaped data=== | |||
Revision as of 21:09, 2 November 2022
Full article title | Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database |
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Journal | npj Computational Materials |
Author(s) | Hatakeyama-Sato, Kan; Umeki, Momoka; Adachi, Hiroki; Kuwata, Naoaki; Hasegawa, Gen; Oyaizu, Kenichi |
Author affiliation(s) | Waseda University, National Institute for Materials Science |
Primary contact | Email: oyaizu at waseda dot jp |
Year published | 2022 |
Volume and issue | 8 |
Article # | 170 |
DOI | 10.1038/s41524-022-00853-0 |
ISSN | 2057-3960 |
Distribution license | Creative Commons Attribution 4.0 International |
Website | https://www.nature.com/articles/s41524-022-00853-0 |
Download | https://www.nature.com/articles/s41524-022-00853-0.pdf (PDF) |
This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Data-driven material exploration is a ground-breaking research style; however, daily experimental results are difficult to record, analyze, and share. We report a data platform that losslessly describes the relationships of structures, properties, and processes as graphs in electronic laboratory notebooks (ELNs). As a model project, organic superionic glassy conductors were explored by recording over 500 different experiments. Automated data analysis revealed the essential factors for a remarkable room-temperature ionic conductivity of 10−4 to 10−3 S cm−1 and a Li+ transference number of around 0.8. In contrast to previous materials research, everyone can access all the experimental results—including graphs, raw measurement data, and data processing systems—at a public repository. Direct data sharing will improve scientific communication and accelerate integration of material knowledge.
Keywords: materials science, materials informatics, electronic laboratory notebook, data sharing
Introduction
Materials informatics is the study of the data-oriented understanding of materials science data, represented by structures, properties, mechanisms, and protocols. [1] Artificial intelligence (AI) has been used in the field for automated material design, massive data analyses, and accelerated experiments with robots to advance the discovery of materials for energy- and environment-related applications. [1,2,3,4,5]
A long-term challenge in materials informatics and materials science is lossless data sharing by the scientific community. [6] Although materials and devices are sensitive to their preparation processes, materials databases and scientific documents generally do not provide sufficient information. [1,7,8] Most databases focus on structure–property relations and ignore or shorten the preparation protocols. [1,4,6,8] Experimental methods are available in scientific journals, but only specialists can appropriately extract the structure–property–process relationships from the text, and automated text parsing by AI is not yet practical. [7,9] Furthermore, detailed information—including non-representative experimental protocols, lot numbers of reagents, and raw measurement data—is often omitted from articles, which leaves major uncertainties about a material's data. As such, researchers may need to improve their communication style to achieve lossless material data sharing.
Given these factors, we propose a data platform that can explicitly describe the relations among the structures, properties, and processes of materials (Fig. 1). Based on the concepts of knowledge graphs or flowcharts [7,10], all experimental events are connected as nodes in graphs. Most experimental information can be described losslessly as graphs, the format of which is also compatible with data science. [7] We demonstrated the system by using it in our research of superionic organic conductors, which revealed the factors for achieving a remarkable room-temperature conductivity of 10−4 to 10−3 S/cm and a Li+ transference number of 0.8, practically the highest values of known tested organic solid-state conductors without plasticizers. [11,12,13,14,15] All experimental data, including everyday experimental operations and measurements (over 500 records), were recorded in the database and are available from a public repository. This work is ultimately representative of the demonstration in experimental materials science of the everything-open research style, which should become the standard for scientific communication to accelerate the integration of materials knowledge.
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Results
Recording daily experiments as graph-shaped data
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.