High-throughput computation of Raman spectra by atomistic first-principles methods
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
IT115 Wetteri auditorium, Linnanmaa campus
Topic of the dissertation
High-throughput computation of Raman spectra by atomistic first-principles methods
Doctoral candidate
Master of Science Mohammad Bagheri
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Microelectronics Research Unit
Subject of study
Computational Physics
Opponent
Professor Ludger Wirtz, University of Luxembourg
Custos
Assistant Professor Hannu-Pekka Komsa, University of Oulu
Construction of a large public database of simulated Raman spectra efficiently and its applications in material analysis
Raman spectroscopy is a powerful tool for studying the properties of materials. To understand Raman spectra, scientists compare them to known references found in research papers or experimental collections. It's also possible to simulate Raman spectra using computational Physics methods, but these methods are usually very demanding and time-consuming. Because of this, there isn't a large database of simulated Raman spectra available.
This doctoral thesis developed a more efficient way to calculate Raman spectra. Using this approach, a large database was created with spectra for 5,099 different materials, including semiconductors and insulators. This database is available to the public on the computational Raman database website (https://ramandb.oulu.fi/).
The new workflow and collected data enabled further research in several areas. New methods for classifying materials based on their dimensionality were developed, which led to the identification of new two-dimensional material candidates. The relationship between the structure of silicates and their Raman spectra was explored, highlighting similarities among materials with similar structures. Finally, the optimized workflow was used to simulate the Raman spectra of new materials, helping scientists identify these materials in experimental data.
This doctoral thesis developed a more efficient way to calculate Raman spectra. Using this approach, a large database was created with spectra for 5,099 different materials, including semiconductors and insulators. This database is available to the public on the computational Raman database website (https://ramandb.oulu.fi/).
The new workflow and collected data enabled further research in several areas. New methods for classifying materials based on their dimensionality were developed, which led to the identification of new two-dimensional material candidates. The relationship between the structure of silicates and their Raman spectra was explored, highlighting similarities among materials with similar structures. Finally, the optimized workflow was used to simulate the Raman spectra of new materials, helping scientists identify these materials in experimental data.
Last updated: 11.9.2024