Full
YANG Mingliang (1996-), female, Doctoral candidate. E-mail: yangmingliang@mail.ustc.edu.cn
Outline
With recent development on various functional crystal materials and large-size crystal preparation technologies, the reliance on experience and technological bottlenecks in traditional crystal growth becomes increasingly prominent. To meet the growing theoretical and technical demands, some methods and technologies for crystal growth are developed. Also, the development of computer science and machine learning technologies provides some opportunities in this field. Machine learning, through the analysis of vast amounts of data, can automatically extract the underlying knowledge and patterns, thereby enabling a prediction of crystal structures and an optimization/control of the crystal growth process.
Crystal structure prediction involves determining the microstructure of a crystal under given chemical compositions. Traditionally, the structure of crystals can be just determined through the related experiments. However, the experimental methods are time-consuming and costly processes. In contrast, machine learning methods can learn from the crystal structural data and predict the structure of crystals without the experiments. A series of crystal structure prediction softwares named CRYSPNet, USPEX, and CALYPSO are developed.
Conventional methods of optimizing growth conditions usually require extensive experience and experimental trial-and-error. Machine learning, via analyzing the existing data on crystal growth, can predict the optimal parameter combinations, guide the selection of parameters in actual production and accelerate the optimization process. Compared to conventional experiments and CFD simulations, machine learning offers faster and more accurate predictions. For instance, model construction based on neural networks is approximately 107 times faster than the CFD simulation. The application of such novel technologies in crystal growth can promote the research and development in the related fields.
The crystal growth process is complex and variable, involving the interaction of multiple factors. Conventional control methods largely rely on experience and actual operations. However, with the application and development of machine learning and automatic control methods, crystal growth control is no longer limited to subjective and empirical judgments, but can leverage the capabilities of computer algorithms and data analysis to achieve more accurate and precise control of crystal growth. Machine learning via utilizing large datasets to train and optimize machine learning models can predict and determine the dynamic changes in crystal growth, and timely adjust and control, thereby achieving an effective control over the crystal growth process.
This review provided a brief summary of the research progress in the application of machine learning to crystal growth, and discussed mainly three aspects, i.e., crystal structure prediction, optimization of crystal growth conditions, and methods of controlling crystal growth. In terms of crystal structure prediction, machine learning achieves significant results, but the accuracy and computational efficiency still need to be improved, especially for large-size, multi-component complex systems. In the future, it is necessary to further improve and develop algorithms to increase the accuracy and computational efficiency of prediction models. Regarding the optimization of crystal growth conditions, machine learning methods can predict the optimum parameter combinations, speeding up the optimization process. However, there are still some issues with data accuracy, process complexity, and model interpretability that need to be addressed. Future work should involve the integration of more machine learning algorithms, combined with theoretical and practical research, to develop reliable and interpretable models. In terms of methods for crystal growth control, machine learning algorithms can precisely control the crystal growth process, improving the stability and quality of crystal growth. However, there are still some challenges such as in-depth studies on the physical mechanisms of crystal growth, the laws of control, and the simulation of complex systems with multiple coupled factors. In the future, it is necessary to combine advanced machine learning algorithms and optimization methods to enhance the simulation of multi-factor coupling and complex systems, further improving the control capability of crystal growth.
12
4
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
