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For: Dropka N, Holena M. Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials. Crystals 2020;10:663. [DOI: 10.3390/cryst10080663] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Number Cited by Other Article(s)
1
Dang Y, Kutsukake K, Liu X, Inoue Y, Liu X, Seki S, Zhu C, Harada S, Tagawa M, Ujihara T. A Transfer Learning‐Based Method for Facilitating the Prediction of Unsteady Crystal Growth. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
2
Xiouras C, Cameli F, Quilló GL, Kavousanakis ME, Vlachos DG, Stefanidis GD. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization. Chem Rev 2022;122:13006-13042. [PMID: 35759465 DOI: 10.1021/acs.chemrev.2c00141] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
3
Liu X, Dang Y, Tanaka H, Fukuda Y, Kutsukake K, Kojima T, Ujihara T, Usami N. Data-Driven Optimization and Experimental Validation for the Lab-Scale Mono-Like Silicon Ingot Growth by Directional Solidification. ACS OMEGA 2022;7:6665-6673. [PMID: 35252661 PMCID: PMC8892659 DOI: 10.1021/acsomega.1c06018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
4
Yamada T, Watanabe T, Hatsusaka K, Yuan J, Koyama M, Teshima K. Importance of raw material features for the prediction of flux growth of Al2O3 crystals using machine learning. CrystEngComm 2022. [DOI: 10.1039/d2ce00010e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
5
Dang Y, Zhu C, Ikumi M, Takaishi M, Yu W, Huang W, Liu X, Kutsukake K, Harada S, Tagawa M, Ujihara T. Adaptive process control for crystal growth using machine learning for high-speed prediction: application to SiC solution growth. CrystEngComm 2021. [DOI: 10.1039/d0ce01824d] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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