Issue 14, 2021

Geometrical design of a crystal growth system guided by a machine learning algorithm

Abstract

In the design of a crystal growth system, the ability to efficiently regulate intertwined geometrical parameters is crucial for its successful development and commercialization. However, the traditional experimental and computational methods consume tremendous amounts of time and resources. To address this problem, a machine learning approach was developed in this study to accelerate the geometry optimization process. It was found that the combination of machine learning with a genetic algorithm could generate various possible solutions through a global search at a relatively high speed, which lie outside the solution range of the experimental optimization methods that are currently used. By applying this technique, an optimal geometrical design was obtained for a 150 mm top-seed solution growth system, indicating that the proposed method represents an innovative and attractive strategy for the development of crystal growth systems with superior characteristics.

Graphical abstract: Geometrical design of a crystal growth system guided by a machine learning algorithm

Article information

Article type
Paper
Submitted
23 jan 2021
Accepted
18 feb 2021
First published
19 feb 2021

CrystEngComm, 2021,23, 2695-2702

Geometrical design of a crystal growth system guided by a machine learning algorithm

W. Yu, C. Zhu, Y. Tsunooka, W. Huang, Y. Dang, K. Kutsukake, S. Harada, M. Tagawa and T. Ujihara, CrystEngComm, 2021, 23, 2695 DOI: 10.1039/D1CE00106J

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