A lossless data management platform for machine learning and experimental data sharing

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

The introduction of physical informatics, a highly data-driven subject focused on materials data, including synthesis techniques, properties, processes and structures, has been a game-changing advance in the field. Artificial intelligence (AI), which allows comprehensive automated analysis of data and design of materials and experiments that can help identify valuable materials, has benefited greatly.

Exploring ionic glass-organic superconductors through process and materials informatics with this lossless graph database. attributed to him: npj math material (2022). DOI: 10.1038 / s41524-022-00853-0

Unfortunately, data loss often results from sharing data back and forth within the scientific community. This is because most material databases and research papers focus more on interactions of structure properties than on important details such as critical experimental techniques.

To address these problems, a group of researchers has created a laboratory data management platform that explains the links between experimental properties, structures, and procedures. This electronic lab notebook represents the observed events and associated environmental parameters as cognitive graphs.

The research, which was published in the journal npj Computational Materials on August 17, 2022, was based on the idea that knowledge graphs might accurately explain experimental data. The group used an AI-based approach to automatically generate tables from 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 investigate superconductivity in organic lithium (Li+)-ion electrolytes to demonstrate the utility of the platform. In the computerized lab notebook, they entered daily raw data from more than 500 successful and unsuccessful tests. Then the data conversion module automatically converted the knowledge graph data into data sets that computers could learn from and examine the correlation between experimental procedures and outcomes. The ideal ionic conductivity at room temperature of 104-103 S/cm and a Li + conversion number of up to 0.8 was achieved thanks to the analysis, which identified the critical factors.

The new data platform enables routine experimental events to be efficiently recorded and stored as graphs, which are subsequently converted into spreadsheets to make room for further AI-based research. Credit goes to Kan Hatakeyama Sato of Waseda University.

The real-time applications are a platform that “will be able to contribute to the creation of safer, higher-capacity batteries with higher performance.”

This study ensures that all information, including experimental results and primary measurement data, is publicly accessible, as well as providing a solid foundation for data-driven research.

The researcher explains its long-term implications: “Researchers from around the world may discover innovative functional materials more quickly if they share raw experimental data. This strategy could accelerate the development of energy-related tools, 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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Asif Razak is an AI journalist and co-founder of Marktechpost, LLC. He is a visionary, entrepreneur, and engineer who aspires to use the power of artificial intelligence for good.

Asif’s latest project is the development of an artificial intelligence media platform (Marktechpost) that will revolutionize how we find relevant news related to artificial intelligence, data science and machine learning.

Asif was featured by Onalytica in “Who’s Who in AI? (Influential Voices & Brands)” as one of the “Influential AI Journalists” (https://onalytica.com/wp-content/uploads/2021/09/Whos-Who- In-AI.pdf). His interview was also featured by Onalytica (https://onalytica.com/blog/posts/interview-with-asif-razzaq/).

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