AI-driven Calculated Bandgap Data Curation in Materials Projects Based on Known Experimental Data

Student: Andreeva Yuliya

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 8

Year of Graduation: 2024

The development of modern machine learning technologies has greatly simplified the creation of databases that contain calculated values of the width of the forbidden zone. The process of empirical creation of such databases involves large financial and time costs. In the Materials Project process, gaps in the values were filled in with large errors. This brings inconveniences when working with the width of the forbidden zone of a substance. Using the Russian consolidated database integrated with Materials Project by API and machine learning model it is possible to fill in the empty values of forbidden zone width and predictions of unknown experimental values to get a more complete and correct database. This project will help to unify and improve efficiency in the interaction between the field of materials science and artificial intelligence.

Full text (added May 20, 2024)

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