In the field of materials science, even small deviations from experimental parameters and protocols can lead to undesirable changes in a material’s properties. A breakthrough development in this field came with the emergence of materials informatics—a highly data-dependent field focused on materials data, including synthetic protocols, properties, mechanisms, and structures. It has benefited significantly from artificial intelligence (AI), enabling large-scale, automated data analysis, materials design, and experiments that can aid in the discovery of useful materials.
Unfortunately, the back and forth exchange of data with the scientific community often results in data loss. This is because most materials databases and research work focus mainly on structure-property relationships and less on important information such as essential experimental protocols.
To address these issues, a research team led by Assistant Professor Kan Hatakeyama-Sato and Professor Kenichi Oyaizu from Waseda University in Japan has developed a laboratory data management platform that describes the relationships between properties, structures and experimental processes in electronic laboratory notebooks. In this electronic laboratory notebook, experimental events and associated environmental parameters are presented as knowledge graphs.
Their study, which was published in npj computer materials on August 17, 2022, relied on the concept that experimental information can be losslessly described as knowledge graphs. The team integrated an AI-based algorithm that could automatically convert these knowledge graphs into spreadsheets and upload them to a public repository. This step was included to ensure lossless data exchange and to give the scientific community better insight into the experimental conditions.
To demonstrate the applicability of this platform, the team used it to study superionic conductivity in organic lithium (Li+)-ionic electrolytes. They entered everyday raw data from over 500 experiments – both successful and unsuccessful – into the electronic laboratory notebook. Next, the data conversion engine automatically converted the Knowledge Graph data into machine-learnable datasets and analyzed the relationship between experimental operations and results. This analysis revealed the important parameters required to achieve an excellent ionic conductivity of 10 at room temperature−4-10−3 S/cm and a Li+ Transmission number as high as 0.8.
So what are the real-time applications of this platform? Hatakeyama-Sato notes: “This platform is currently applicable to solid-state batteries and will contribute to the development of safer and higher-performing batteries with improved performance.”
This study not only provides a platform for reliable data-driven research, but ensures that all information, including experimental results and raw measurement data, is publicly available for everyone.
Regarding the long-term effects, Hatakeyama-Sato further adds: “Through share raw experimental data under Researchers around the world, novel functional materials could be discovered faster. This approach can also accelerate the development of energy-related devices, including next-generation batteries and solar cells.”
A small step towards direct and complete data sharing is definitely a big step towards materials research!
Authors: Kan Hatakeyama-Sato1Momoka Umeki1Hiroki Adachi1Naoaki Kuwata2General Hasegawa2 and Kenichi Oyaizu1
1Department of Applied Chemistry, Waseda University
2National Institute for Materials Science (NIMS)
About Waseda University
Located in the heart of Tokyo, Waseda University is a leading private research university dedicated since 1882 to academic excellence, innovative research and civic engagement on both a local and global scale. The university ranks number one in Japan for international activities, including the number of international students, with the widest range of programs taught entirely in English. To learn more about Waseda University, visit https://www.waseda.jp/top/en
npj computer materials
subject of research
Exploration of organic superionic glass conductors by process and material informatics with lossless graph database
Article publication date
August 17, 2022
The authors declare no competing interests
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