TY  - INPR
ID  - SisLab4234
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4234/
A1  - Kubo, Masatoshi
A1  - Nguyen, Hai Chau
A1  - Brik, Sergey
A1  - Yamamoto, Tomoyuki
Y1  - 2020/12/09/
N2  - Garnet structured compounds have been widely utilized as the host materials of phosphors. Due to their complicated structures with numerous atoms in a unit cell of the garnet structure, it is difficult to predict their crystal structures accurately by the first principles calculation, especially when they include magnetic elements. It was proposed to predict the structural parameters for spinel compounds by the empirical model employing ionic radii
and electronegativities, which reproduced the experimental structural parameters of spinels successfully. In the present work, we propose a comprehensive and reliable model to explain and/or predict the structural parameters of the garnet structured materials using the machine learning. The lattice parameters of 182 garnet compounds reported in the database are compiled in our dataset to train the model. Using the ionic radii and electronegativities of the constituent elements of considered garnet compounds, we constructed the linear regression model fitted to the garnets' lattice constants in the dataset. The predicted values obtained as a result of the training of the model exhibited high correlation with the actual lattice constants, having the correlation coefficient of 0.988. For each composition in our dataset, the relative error between experimental and calculated lattice constants was less than 1.60%.
TI  - Modelling of Lattice Constants of the Garnet Structured Compounds Using Machine Learning
M2  - online
AV  - none
T2  - 30th annual meeting of Material Research Society of Japan, 2020 (MRS-J 2020)
ER  -