The combination of quantum computing (QC) with artificial intelligence (AI) has the potential to significantly transform several industries and enhance quality of life. AI applications, such as autonomous vehicles and image recognition, significantly depend on supervised learning, which is a type of machine learning. Researchers are now examining the intersection of AI and multi-agent planning systems (MAPS) using quantum algorithms and game theory. Quantum machine learning and optimization techniques use the advantages of QC to accelerate training and optimization processes, drawing inspiration from quantum physics. With machine learning methods, it is now becoming commonplace in materials research to make predictions about the characteristics of inorganic solid-state materials. However, a significant issue lies in the limited availability of datasets. This article explores the structure and characteristics of the High Throughput Experimental Materials (HTEM) Database, which contains a vast amount of experimental data on inorganic materials obtained using high-throughput technologies. The HTEM Database provides a significant resource by offering comprehensive EDIM built using high-throughput exploration methodologies. The database offers a multitude of search and visualization functionalities and may be accessible using a web-based interface. Regular updates are performed on the HTEM DB to integrate new data and mitigate processing issues.
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Center for Research and Development, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.
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Cite this article
Anandakumar Haldorai and Shrinand Anandakumar, “Revolutionizing Materials Research with Quantum Computation: The Role of High-Throughput Experimental Materials Databases”, Journal of Computational Intelligence in Materials Science, vol.2, pp. 107-118, 2024. doi: 10.53759/832X/JCIMS202402011.
