• Journal of the Chinese Ceramic Society
  • Vol. 52, Issue 7, 2412 (2024)
YANG Mingliang1,2,3, WANG Ruixian1,2,3, SUN Guihua1,3, WANG Xiaofei1,3..., DOU Renqin1,3, HE Yi1,2,3 and ZHANG Qingli1,3,*|Show fewer author(s)
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    DOI: 10.14062/j.issn.0454-5648.20230621 Cite this Article
    YANG Mingliang, WANG Ruixian, SUN Guihua, WANG Xiaofei, DOU Renqin, HE Yi, ZHANG Qingli. Research Progress on the Application of Machine Learning in Crystal Growth[J]. Journal of the Chinese Ceramic Society, 2024, 52(7): 2412 Copy Citation Text show less
    References

    [1] ZHANG Shouqing. Chin Sci Bull, 1964(9): 787-797.

    [2] ZHENG Yanqing, SHI Erwei, LI Wenjun, et al. J Inorg Mater, 1999, 14(3): 321-332.

    [3] SUN Deyun. Study on the growth mechanism of cerium carbonate crystals simulated by AIMD[D]. Baotou: Inner Mongolia University of Science & Technology, 2022.

    [4] YANG Jinfeng, SUN Jun, QIN Juan, et al. J Synth Cryst, 2022, 51(9/10): 1541-1559.

    [5] WANG J Y, YU H H, WU Y C, et al. Recent developments in functional crystals in China[J]. Engineering, 2015, 1(2): 192-210.

    [6] XUE D F, SUN C T. Study on the crystallization process of function inorganic crystal materials[J]. Sci Sin-Tech, 2014, 44(11): 1123-1136.

    [7] MA Z Z, CHEN Y Z, HE Y, et al. Growth, thermal properties and laser performance of Er, Pr:Y2.8Sc1Al4.2O12: A promising multi-wavelength laser crystal[J]. Appl Phys A, 2021, 127(7): 1-9.

    [8] DING S J, REN H, ZOU Y, et al. Single crystal growth and property investigation of Dy3+ and Tb3+ co-doped Gd3Sc2Al3O12 (GSAG): Multiple applications for GaN blue LD pumped all-solid-state yellow lasers and UV or blue light chip excited solid-state lighting[J]. J Mater Chem C, 2021, 9(30): 9532-9538.

    [9] CHEN X F, YANG X L, XIE X J, et al. Research progress of large size SiC single crystal materials and devices[J]. Light Sci Appl, 2023, 12(1): 28.

    [10] ZHANG C Q, WANG J Y, HU X B, et al. Growth of large K2Al2B2O7 crystals[J]. J Cryst Growth, 2002, 235(1/4): 1-4.

    [11] CHERNOV A A, MüLLER KRUMBHAAR H. Modern theory of crystal growth I[M]. Springer Science & Business Media, 2012.

    [12] TILLER W A. The science of crystallization: macroscopic phenomena and defect generation[M]. Cambridge: Cambridge University Press, 1991.

    [17] XU Yadong. J Synth Cryst, 2022, 51(9/10): 1519-1522.

    [18] YAO T S, TANG C Y, YANG M, et al. Machine learning to instruct single crystal growth by flux method[J]. Chin Phys Lett, 2019, 36(6): 068101.

    [19] MIAO L, WANG L W. Liquid to crystal Si growth simulation using machine learning force field[J]. J Chem Phys, 2020, 153(7): 074501.

    [20] ZHANG J M, HARMAN M, MA L, et al. Machine learning testing: Survey, landscapes and horizons[J]. IEEE Trans Softw Eng, 2022, 48(1): 1-36.

    [21] AMERSHI S, BEGEL A, BIRD C, et al. Software engineering for machine learning: A case study[C]//2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). Montreal, QC, Canada. IEEE, 2019: 291-300.

    [22] KIM S, NOH J, GU G H, et al. Generative adversarial networks for crystal structure prediction[J]. ACS Cent Sci, 2020, 6(8): 1412-1420.

    [23] WOODLEY S M. Prediction of crystal structures using evolutionary algorithms and related techniques[M]//Applications of Evolutionary Computation in Chemistry. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004: 95-132.

    [24] REVARD B C, TIPTON W W, HENNIG R G. Structure and stability prediction of compounds with evolutionary algorithms[J]. Top Curr Chem, 2014, 345: 181-222.

    [25] LIANG Jing, LIU Rui, QU Boyang, et al. J Zhengzhou Univ Eng Sci, 2018, 39(3): 15-21.

    [26] HANSEN N. The CMA evolution strategy: A comparing review[M]// Towards a New Evolutionary Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007: 75-102.

    [27] LIEPINS G E, HILLIARD M R. Genetic algorithms: foundations and applications[J]. Ann Oper Res, 1989, 21(1): 31-57.

    [28] GREFENSTETTE J J. Genetic algorithms and machine learning[C]// Proceedings of the sixth annual conference on Computational learning theory. Santa Cruz, California, USA. New York: ACM, 1993: 3-4.

    [29] POLI R, KENNEDY J, BLACKWELL T. Particle swarm optimization[J]. Swarm Intell, 2007, 1(1): 33-57.

    [30] JAIN M, SAIHIPAL V, SINGH N, et al. An overview of variants and advancements of PSO algorithm[J]. Appl Sci, 2022, 12(17): 8392.

    [31] LI G Q, WANG T, CHEN Q, et al. A survey on particle swarm optimization for association rule mining[J]. Electronics, 2022, 11(19): 3044.

    [32] ELSHEIKH A H, ABD ELAZIZ M. Review on applications of particle swarm optimization in solar energy systems[J]. Int J Environ Sci Technol, 2019, 16(2): 1159-1170.

    [33] HU J J, YANG W H, DONG R Z, et al. Contact map based crystal structure prediction using global optimization[J]. CrystEngComm, 2021, 23(8): 1765-1776.

    [34] STORN R, PRICE K. Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces[J]. J Glob Optim, 1997, 11(4): 341-359.

    [35] ZOU J M, HAN Y, SO S S. Overview of artificial neural networks[M]// Artificial Neural Networks, 2008: 14-22.

    [36] ZHANG Z H. Artificial neural network[M]//Multivariate Time Series Analysis in Climate and Environmental Research. Cham: Springer, 2018: 1-35.

    [37] DONGARE A, KHARDE R R, KACHARE A D. Introduction to artificial neural network[J]. International J Eng Innov Technol (IJEIT), 2012, 2(1): 189-194.

    [38] WANG Na, WANG Jingkang, FENG Han, et al. Chemical Industry and Engineering, 2023, 40(1): 2-14.

    [39] CHENG G J, GONG X G, YIN W J. Crystal structure prediction by combining graph network and optimization algorithm[J]. Nat Commun, 2022, 13(1): 1492.

    [40] GRASER J, KAUWE S K, SPARKS T D. Machine learning and energy minimization approaches for crystal structure predictions: A review and new horizons[J]. Chem Mater, 2018, 30(11): 3601-3612.

    [41] RYAN K, LENGYEL J, SHATRUK M. Crystal structure prediction via deep learning[J]. J Am Chem Soc, 2018, 140(32): 10158-10168.

    [42] LIANG H T, STANEV V, KUSNE A G, et al. CRYSPNet: Crystal structure predictions via neural networks[J]. Phys Rev Materials, 2020, 4(12): 123802.

    [43] GLASS C W, OGANOV A R, HANSEN N. USPEX—evolutionary crystal structure prediction[J]. Comput Phys Commun, 2006, 175(11/12): 713-720.

    [44] WANG Y C, LV J A, ZHU L, et al. CALYPSO: A method for crystal structure prediction[J]. Comput Phys Commun, 2012, 183(10): 2063-2070.

    [45] YAMASHITA T, KANEHIRA S, SATO N, et al. CrySPY: A crystal structure prediction tool accelerated by machine learning[J]. Sci Technol Adv Mater Meth, 2021, 1(1): 87-97.

    [46] CURTIS F, LI X Y, ROSE T, et al. GAtor: A first-principles genetic algorithm for molecular crystal structure prediction[J]. J Chem Theory Comput, 2018, 14(4): 2246-2264.

    [47] NOUIRA A, SOKOLOVSKA N, CRIVELLO J C. Crystalgan: Learning to discover crystallographic structures with generative adversarial networks. arXiv. https://arxiv.org/abs/1810.11203 (2018).

    [48] LONIE D C, ZUREK E. XtalOpt: An open-source evolutionary algorithm for crystal structure prediction[J]. Comput Phys Commun, 2011, 182(2): 372-387.

    [49] SONG Y Q, SIRIWARDANE E M D, ZHAO Y, et al. Computational discovery of new 2D materials using deep learning generative models[J]. ACS Appl Mater Interfaces, 2021, 13(45): 53303-53313.

    [50] LEE I, KIM J, PARK T, et al. Predicting mechanical properties of newly generated two-dimensional materials with minimum uncertainty[J]. Mater Today Adv, 2023, 18: 100374.

    [51] KUKLIN M S, KARTTUNEN A J. Crystal structure prediction of magnetic transition-metal oxides by using evolutionary algorithm and hybrid DFT methods[J]. J Phys Chem C Nanomater Interfaces, 2018, 122(43): 24949-24957.

    [52] PAKHNOVA M, KRUGLOV I, YANILKIN A, et al. Search for stable cocrystals of energetic materials using the evolutionary algorithm USPEX[J]. Phys Chem Chem Phys, 2020, 22(29): 16822-16830.

    [53] ZHANG W W, OGANOV A R, GONCHAROV A F, et al. Unexpected stable stoichiometries of sodium chlorides[J]. Science, 2013, 342(6165): 1502-1505.

    [54] WANG Y C, LV J, GAO P Y, et al. Crystal structure prediction via efficient sampling of the potential energy surface[J]. Acc Chem Res, 2022, 55(15): 2068-2076.

    [55] GAO B, GAO P Y, LU S H, et al. Interface structure prediction via CALYPSO method[J]. Sci Bull, 2019, 64(5): 301-309.

    [56] LU S H, WANG Y C, LIU H Y, et al. Self-assembled ultrathin nanotubes on diamond (100) surface[J]. Nat Commun, 2014, 5: 3666.

    [57] YANG J N, XIAO Y, KUANG X Y. The local structure and electronic properties of Ho3+-doped BaY2F8: A first-principles method[J]. Mater Chem Phys, 2023, 298: 127459.

    [58] MIYAZAWA H, LIU L J, KAKIMOTO K. Numerical analysis of influence of crucible shape on interface shape in a unidirectional solidification process[J]. J Cryst Growth, 2008, 310(6): 1142-1147.

    [59] LI Z Y, LIU L J, ZHANG Y F, et al. Preservation of seed crystals in feedstock melting for cast quasi-single crystalline silicon ingots[J]. Int J Photoenergy, 2013, 2013: 1-7.

    [60] FüHNER T, JUNG T. Use of genetic algorithms for the development and optimization of crystal growth processes[J]. J Cryst Growth, 2004, 266(1/3): 229-238.

    [61] SU J A, CHEN X J, LI Y A, et al. A niching genetic algorithm applied to optimize a SiC-bulk crystal growth system[J]. J Cryst Growth, 2017, 468: 914-918.

    [62] LIIRI M, ENQVIST Y, KALLAS J, et al. CFD modelling of single crystal growth of potassium dihydrogen phosphate (KDP) from binary water solution at 30 ℃[J]. J Cryst Growth, 2006, 286(2): 413-423.

    [63] LI Z X, SMIRNOV A. Application of computer modeling to pulling rate and productivity of Czochralski pullers in PV Si crystal growth[J]. J Cryst Growth, 2023, 611: 127178.

    [64] RYU J H, LEE W J, LEE Y C, et al. CFD analysis for effects of the crucible geometry on melt convection and growth behavior during sapphire single crystal growth by Kyropoulos process[J]. J Korean Cryst Growth Cryst Technol, 2012, 22(3): 115-121.

    [65] MU Honghe, WANG Pengfei, SHI Yufeng, et al. J Inorg Mater (in Chinese), 2023, 38(3): 288-295.

    [66] BOUCETTA A, KUTSUKAKE K, KOJIMA T, et al. Application of artificial neural network to optimize sensor positions for accurate monitoring: An example with thermocouples in a crystal growth furnace[J]. Appl Phys Express, 2019, 12(12): 125503.

    [67] HAO Peiyao, ZHENG Lili, ZHANG Hui, et al. J Synth Cryst, 2022, 51(8): 1323-1336.

    [68] YU W C, ZHU C, TSUNOOKA Y, et al. Geometrical design of a crystal growth system guided by a machine learning algorithm[J]. CrystEngComm, 2021, 23(14): 2695-2702.

    [69] KUTSUKAKE K, NAGAI Y T, BANBA H. Virtual experiments of Czochralski growth of silicon using machine learning: Influence of processing parameters on interstitial oxygen concentration[J]. J Cryst Growth, 2022, 584: 126580.

    [70] ASADIAN M, SEYEDEIN S H, ABOUTALEBI M R, et al. Optimization of the parameters affecting the shape and position of crystal-melt interface in YAG single crystal growth[J]. J Cryst Growth, 2009, 311(2): 342-348.

    [71] ZHANG Jing, PAN Yani, LIU Ding, et al. J Synth Cryst, 2018, 47(12): 2429-2435.

    [72] TSUNOOKA Y, KOKUBO N, HATASA G, et al. High-speed prediction of computational fluid dynamics simulation in crystal growth[J]. CrystEngComm, 2018, 20(41): 6546-6550.

    [73] QI X F, MA W C, DANG Y F, et al. Optimization of the melt/crystal interface shape and oxygen concentration during the Czochralski silicon crystal growth process using an artificial neural network and a genetic algorithm[J]. J Cryst Growth, 2020, 548: 125828.

    [74] LIU Ding, ZHAO Xiaoguo, ZHAO Yue. Contr Theory Appl, 2017, 34(1): 1-12.

    [75] ZHANG N, LIU D, WAN Y. Numerical modeling and control of the dynamic single silicon crystal growth process[J]. IEEE Trans Semicond Manuf, 2021, 34(1): 94-103.

    [76] SONG D J, TAN B X, LIU J C. Improvement of crystal growth control system[J]. Adv Mater Res, 2012, 433-440: 7569-7573.

    [77] WANG Qing, JIANG Lei, ZHANG Tingman. J Synth Cryst, 2011, 40(5): 1353-1357.

    [78] LI Jianhong, JI Wengang, SONG Xing, et al. J Synth Cryst, 2019, 48(8): 1438-1444.

    [79] REN Junchao, LIU Ding, WAN Yin. Acta Autom Sin, 2020, 46(5): 1004-1016.

    [80] LIN Guangwei, WANG Shan, ZHANG Xiya, et al. J Synth Cryst (in Chinese), 2022, 51(2): 229-241.

    [81] LIU D, ZHANG N, JIANG L, et al. Nonlinear generalized predictive control of the crystal diameter in CZ-Si crystal growth process based on stacked sparse autoencoder[J]. IEEE Trans Contr Syst Technol, 2020, 28(3): 1132-1139.

    [82] WAN Y, LIU D, REN J C. Iterative learning-based predictive control method for electronic grade silicon single crystal batch process[J]. IEEE Trans Semicond Manuf, 2023, 36(2): 239-250.

    [83] DANG Y F, ZHU C, IKUMI M, et al. Adaptive process control for crystal growth using machine learning for high-speed prediction: application to SiC solution growth[J]. CrystEngComm, 2021, 23(9): 1982-1990.

    YANG Mingliang, WANG Ruixian, SUN Guihua, WANG Xiaofei, DOU Renqin, HE Yi, ZHANG Qingli. Research Progress on the Application of Machine Learning in Crystal Growth[J]. Journal of the Chinese Ceramic Society, 2024, 52(7): 2412
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