Journal:Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database

Full article title	Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database
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)

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.

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.

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.