Importance of raw material features for the prediction of flux growth of Al<sub>2</sub>O<sub>3</sub> crystals using machine learning-Reference-Cited by-同舟云学术

Importance of raw material features for the prediction of flux growth of Al₂O₃ crystals using machine learning

Published:2022 Issue:17 Volume:24 Page:3179-3188
ISSN:1466-8033
Container-title:CrystEngComm
language:en
Short-container-title:CrystEngComm

Author:

Yamada Tetsuya¹²^ORCID,Watanabe Takanori³,Hatsusaka Kazuaki³,Yuan Jianjun³,Koyama Michihisa¹^ORCID,Teshima Katsuya¹²^ORCID

Affiliation:

1. Research Initiative for Supra-Materials, Shinshu University, 4-17-1 Wakasato, Nagano 380-8553, Japan

2. Department of Materials Chemistry, Faculty of Engineering, Shinshu University, 4-17-1 Wakasato, Nagano 380-8553, Japan

3. Central Research Laboratories, DIC Corporation, 631, Sakado, Sakura, Chiba 285-8668, Japan

Abstract

We evaluated the role of raw-material features for machine-learning prediction of the flux crystal growth of Al2O3 in MoO3 based on 185 types of growth trials.

Funder

Ministry of Education, Culture, Sports, Science and Technology

Japan Science and Technology Agency

Shinshu University

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Materials Science,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CE/D2CE00010E

Reference28 articles.

1. Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

2. High-speed prediction of computational fluid dynamics simulation in crystal growth

3. Application of artificial neural network to optimize sensor positions for accurate monitoring: an example with thermocouples in a crystal growth furnace

4. Optimization of the controlling recipe in quasi-single crystalline silicon growth using artificial neural network and genetic algorithm

5. Machine Learning to Instruct Single Crystal Growth by Flux Method*

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