A Platform for Managing Lossless Data for Machine Learning and Sharing Experimental Data

The study of the data-oriented understanding of materials data, represented by structures, properties, mechanisms, and protocols, is known as materials informatics, the development of materials for energy and environmental applications.

The introduction of materials informatics, a highly data-dependent discipline focused on materials data, including synthesis techniques, properties, processes, and structures, was a breakthrough advance in this field. He has benefited greatly from artificial intelligence (AI), which enables large-scale, automated data analysis, materials design, and experiments that can help identify valuable materials.

Unfortunately, data loss often results from the back and forth exchange of data within the scientific community. This is because most materials databases and research papers place more emphasis on structure-property interactions than crucial details such as critical experimental techniques.

To address these issues, a group of researchers created a laboratory data management platform that explains the relationships between properties, structures and experimental procedures. This electronic laboratory notebook presents observed events and associated environmental parameters as knowledge graphs.

Research, published August 17, 2022 in the journal npj Computational Materials, was based on the idea that knowledge graphs can accurately explain experimental data. The group used an AI-based method to automatically create tables from these knowledge graphs and publish them to a public repository. This procedure was added to ensure lossless data transmission and to give the scientific community a better understanding of the experimental setup.

The team used this platform to study superionic conductivity in lithium (Li+) ion organic electrolytes to demonstrate the utility of the platform. They entered raw data from more than 500 successful and unsuccessful tests daily into the computer-assisted laboratory notebook. The data conversion engine then automatically converted the knowledge graph data into datasets from which computers can learn and explored the link between experimental procedures and results. Thanks to the analysis that identified the critical factors, an ideal ionic conductivity of 104-103 S/cm at room temperature and a Li+ transfer number of up to 0.8 were achieved.

The new data platform enables routine experimental events to be efficiently recorded and stored as graphs, which are then converted into data tables to make room for additional AI-based research. Thanks to Kan Hatakeyama-Sato from Waseda University.

Real-time applications are a platform that “can help create safer, more powerful, high-capacity batteries.”

This study ensures that all information, including experimental results and raw measurement data, is made publicly available and provides a solid foundation for data-driven research.

The researcher explains the long-term effects: “Researchers from all over the world could discover innovative functional materials faster if they share experimental raw data. This strategy can accelerate the development of energy-related devices such as next-generation solar cells and batteries.”

This Article is written as a research summary article by Marktechpost Staff based on the research paper 'Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database'. All Credit For This Research Goes To Researchers on This Project. Check out the paper and reference article.

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