The study of data-driven understanding of material data, as represented by structures, properties, mechanisms, and protocols, is known as Materials Informatics1. Automated materials design, in-depth data analysis, and accelerated testing using robots have all been used in the field to promote the development of materials for energy and environmental applications.
The introduction of Materials Informatics, a highly data-driven subject focusing on materials data including synthesis techniques, characteristics, processes and structures, has been a revolutionary advancement in this field. Artificial intelligence (AI), which enables in-depth and automated data analysis, material design and experimentation that can help identify valuable materials, has benefited greatly.
Unfortunately, data loss often results from data exchanges within the scientific community. Indeed, most materials databases and research articles focus more on structure-property interactions than on crucial details such as crucial experimental techniques.
To solve these problems, a group of researchers created a laboratory data management platform that explains the links between properties, structures and experimental procedures. This electronic laboratory notebook represents the observed events and the associated environmental parameters in the form of knowledge graphs.
The research, published in the journal npj Computational Materials on August 17, 2022, 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 organic lithium (Li+)-ion electrolytes to show the usefulness of the platform. Into the computerized lab notebook, they entered raw data from more than 500 passed and failed tests daily. The data conversion module then automatically converted the knowledge graph data into datasets that computers can learn from and examined the link between experimental procedures and results. An ideal room temperature ionic conductivity of 104–103 S/cm and a Li+ transfer number of up to 0.8 were achieved through the analysis, which identified the critical factors.
The new data platform allows routine experimental events to be efficiently recorded and stored as graphs, which are then converted into data tables to make room for further AI-based research. Credit goes to Kan Hatakeyama-Sato of Waseda University.
Real-time applications are a platform that “could contribute to the creation of safer, high-capacity batteries with increased performance”.
This study ensures that all information, including experimental results and raw measurement data, is made publicly available, in addition to providing a solid basis for data-driven research.
The researcher explains its long-term impact: “Researchers around the world could discover innovative functional materials more quickly if they shared raw experimental data. This strategy can accelerate the development of energy-related gadgets, 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. Please Don't Forget To Join Our ML Subreddit
Asif Razzaq 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 venture is the development of an artificial intelligence media platform (Marktechpost) that will revolutionize the way people can find relevant news related to artificial intelligence, data science and technology. machine learning.
Asif was featured by Onalytica in its ‘Who’s Who in AI? (Influential Voices & Brands)’ as one of the ‘Influential Journalists in AI’ (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/).