relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4234/
title: Modelling of Lattice Constants of the Garnet Structured Compounds Using Machine Learning
creator: Kubo, Masatoshi
creator: Nguyen, Hai Chau
creator: Brik, Sergey
creator: Yamamoto, Tomoyuki
subject: Information Technology (IT)
subject: Engineering Physics
description: 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%.
date: 2020-12-09
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Kubo, Masatoshi and Nguyen, Hai Chau and Brik, Sergey and Yamamoto, Tomoyuki  (2020) Modelling of Lattice Constants of the Garnet Structured Compounds Using Machine Learning.  In: 30th annual meeting of Material Research Society of Japan, 2020 (MRS-J 2020), 9-11 Dec 2020, online.    (In Press)