Research Progress on Application of Machine Learning in Molecular Beam Epitaxy Growth

doi:10.16553/j.cnki.issn1000-985x.2024.0272

Abstract

Abstract: In recent years， artificial intelligence has been widely applied in the field of materials， and the application of machine learning in molecular beam epitaxy （MBE） has attracted attention. Intelligent recognition and feedback based on in-situ reflection high-energy electron diffraction （RHEED） and related material properties in MBE technology can significantly improve the quality and efficiency of material growth， leading to the realization of intelligent epitaxy of epitaxial films. This article focuses on the application of machine learning in MBE. It first introduces commonly used machine learning algorithm models in MBE， and explains the application of machine learning in optimizing growth conditions and specifically summarizes the research progress on machine learning based on RHEED images for different material systems （semiconductor thin films and quantum structure materials， oxide materials， and two-dimensional materials）. A summary and outlook were provided on the existing problems and future development strategies.

Key words: molecular beam epitaxy; machine learning; reflection high-energy electron diffraction; Ⅲ-Ⅴ semiconductor material; artificial intelligence

CLC Number:

O484.1

YANG Zaihong, ZHOU Can, FAN Liuyan, ZHANG Yanhui, CHEN Zezhong, CHEN Pingping. Research Progress on Application of Machine Learning in Molecular Beam Epitaxy Growth[J]. Journal of Synthetic Crystals, 2025, 54(6): 924-934.

Figures/Tables 11

References 53

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Algorithmic model	Peculiarity	Reference
Random forest	It can process a large amount of data and high-dimensional features， is not easy to overfit， and is robust to missing data. But the training time is long， and the interpretability of the model is relatively poor.	［21⁃22］
K-means clustering	Simple， easy to understand and implement， suitable for large-scale datasets. But it is sensitive to the initial clustering center and converges to the local optimal solution.	［23］
Hierarchical clustering	Ability to organize and present information clearly， making complex concepts and relationships easier to understand and remember. But it can be an oversimplification of complex realities， leading to some information being overlooked or misunderstood.	［24］
Nonnegative matrix factorization	The latent features in the data can be extracted.But the computational complexity is high， and it is easy to fall into the local optimal solution.	［25］
Convolutional neural network	It can effectively process two-dimensional data such as images and videos， and has the characteristics of parameter sharing and local connection， which is suitable for extracting spatial features. But the training time is long， the model is poorly interpretable， and it is easy to overfit and other problems.	［26⁃27］
Support vector machine	Able to process high-dimensional data， has good generalization ability， performs well for small-sample data.But has high computational complexity for large-scale datasets， and is sensitive to missing data.	［28］
Principal components analysis	It can reduce data dimensions， remove noise and redundant information， improve model efficiency， and more. But some information may be lost， sensitivity to outliers， etc.	［29］
Uniform manifold approximation and projection	It has a good ability to express the data of nonlinear structure， and has a fast calculation speed. But the processing capacity for ultra-large-scale data is relatively weak.	［30］
Logistic regression	It is suitable for binary classification problems， which is fast to computation， easy to explain and implement.But it is not suitable for nonlinear relationships and is susceptible to outliers.	［31］
Naive bayes	The model is simple， computationally efficient， and performs well with small-scale data. But the conditional independence of the input data is more stringent， and when the assumption is not true， the classification results will be affected.	［32⁃33］

Algorithmic model	Peculiarity	Reference
Random forest	It can process a large amount of data and high-dimensional features， is not easy to overfit， and is robust to missing data. But the training time is long， and the interpretability of the model is relatively poor.	［21⁃22］
K-means clustering	Simple， easy to understand and implement， suitable for large-scale datasets. But it is sensitive to the initial clustering center and converges to the local optimal solution.	［23］
Hierarchical clustering	Ability to organize and present information clearly， making complex concepts and relationships easier to understand and remember. But it can be an oversimplification of complex realities， leading to some information being overlooked or misunderstood.	［24］
Nonnegative matrix factorization	The latent features in the data can be extracted.But the computational complexity is high， and it is easy to fall into the local optimal solution.	［25］
Convolutional neural network	It can effectively process two-dimensional data such as images and videos， and has the characteristics of parameter sharing and local connection， which is suitable for extracting spatial features. But the training time is long， the model is poorly interpretable， and it is easy to overfit and other problems.	［26⁃27］
Support vector machine	Able to process high-dimensional data， has good generalization ability， performs well for small-sample data.But has high computational complexity for large-scale datasets， and is sensitive to missing data.	［28］
Principal components analysis	It can reduce data dimensions， remove noise and redundant information， improve model efficiency， and more. But some information may be lost， sensitivity to outliers， etc.	［29］
Uniform manifold approximation and projection	It has a good ability to express the data of nonlinear structure， and has a fast calculation speed. But the processing capacity for ultra-large-scale data is relatively weak.	［30］
Logistic regression	It is suitable for binary classification problems， which is fast to computation， easy to explain and implement.But it is not suitable for nonlinear relationships and is susceptible to outliers.	［31］
Naive bayes	The model is simple， computationally efficient， and performs well with small-scale data. But the conditional independence of the input data is more stringent， and when the assumption is not true， the classification results will be affected.	［32⁃33］

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