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Abstract

Photoelectron spectroscopy serves as a prevalent characterization technique in the field of materials science. Especially, angle-resolved photoelectron spectroscopy (ARPES) provides a direct method for determining the energy-momentum dispersion relationship and Fermi surface structure of electrons in a material system, therefore ARPES has become a potent tool for investigating many-body interactions and correlated quantum materials. With the emergence of technologies such as time-resolved ARPES and nano-ARPES, the field of photoelectron spectroscopy continues to advance. Meanwhile, the development of synchrotron radiation facilities has led to an increase of high-throughput and high-dimensional experimental data. This highlights the urgency for developing more efficient and accurate data processing methods, as well as extracting deeper physical information. In light of these developments, machine learning will play an increasingly significant role in various fields, including but not limited to ARPES. This paper reviews the applications of machine learning in photoelectron spectroscopy, mainly including the following three aspects. 1) Data Denoising 　Machine learning can be utilized for denoising photoelectron spectroscopy data. The denoising process via machine learning algorithms can be divided into two methods. Neither of the two methods need manual data annotation. The first method is to use noise generation algorithms to simulate experimental noise, so as to obtain effective low signal-to-noise ratio data pair to high signal-to-noise ratio data pair. And the second method is to extract noise and clean spectral data. 2) Electronic Structure and Chemical Composition Analysis　Machine learning can be used for analyzing electronic structure and chemical composition. (Angle-resolved) photoelectron spectroscopy contains abundant information about material structure. Information such as energy band structure, self-energy, binding energy, and other condensed matter data can be rapidly acquired through machine learning schemes. 3) Prediction of Photoelectron Spectroscopy　The electronic structure information obtained by combining first-principles calculation can also predict the photoelectron spectroscopy. The rapid acquisition of photoelectron spectroscopy data through machine learning algorithms also holds significance for material design. Photoelectron spectroscopy holds significant importance in the study of condensed matter physics. In the context of the development of synchrotron radiation, the construction of an automated data acquisition and analysis system can play a pivotal role in studying condensed matter physics. In addition, adding more physical constraints to the machine learning model will improve the interpretability and accuracy of the model. There exists a close relationship between photoelectron spectroscopy and first-principles calculations of electronic structure properties. The integration of these two through machine learning is anticipated to significantly contribute to the study of electronic structure properties. Furthermore, as machine learning algorithms continue to evolve, the application of more advanced machine learning algorithms in photoelectron spectroscopy research is expected. Building automated data acquisition and analysis systems, designing comprehensive workflows based on machine learning and first-principles methods, and integrating new machine learning techniques will help accelerate the progress of photoelectron spectroscopy experiments and facilitate the analysis of electronic structure properties and microscopic physical mechanisms, thereby advancing the frontier research in quantum materials and condensed matter physics.

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Authors and contacts

Corresponding author:Zhao Li-Na,linazhao@ihep.ac.cn

Funds:Project supported by the National Key Research and Development Program of China (Grant No. 2021YFA1200904), the National Natural Science Foundation of China (Grant Nos. 12375326, 62205338), and the Technological Innovation Program of the Institute of High Energy Physics of the Chinese Academy of Sciences (Grant No. E35457U210).

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Vol. 73, Issue 21 - 2024-11-05

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