Geometrical design of a crystal growth system guided by a machine learning algorithm. Issue 14 (23rd February 2021)

• Record Type: Journal Article
• Authors: Yu, Wancheng; Zhu, Can; Tsunooka, Yosuke; Huang, Wei; Dang, Yifan; Kutsukake, Kentaro; Harada, Shunta; Tagawa, Miho; Ujihara, Toru
• Is Part Of: CrystEngComm. Volume 23:Issue 14(2021)
• Page Start: 2695
• Page End: 2702
• Publication Date: 2021-02-23
• Subjects: Crystals -- Periodicals; Crystal growth -- Periodicals; Crystallography -- Periodicals; Cristaux -- Périodiques; Cristaux -- Croissance -- Périodiques; Cristallographie -- Périodiques; 548
• DOI: 10.1039/d1ce00106j
• Languages: English
• ISSNs: 1466-8033

Abstract

Abstract : This study proposes a new high-speed method for designing crystal growth systems. It is capable of optimizing large numbers of parameters simultaneously which is difficult for traditional experimental and computational techniques. 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.