A review article titled "Machine learning empowered surface growth of 2D materials: Synthesis, characterization and mechanism" by Professor Zhou Miao's team from the School of Physics at Beihang University has been published online in Surface Science Reports, a leading review journal in the field of surface science.

The advent of atomically thin two-dimensional (2D) materials provides a versatile platform to transcend the fundamental limitations of silicon-based electronics and continue the miniaturization of field-effect transistors, yet the epitaxial growth of wafer-scale, single-crystalline structure remains a formidable challenge. In recent years, the vigorous development of machine learning (ML) techniques has contributed to a revolutionary shift in materials synthesis, characterization and application, offering unprecedented opportunities for scientific and technological innovations that are inaccessible through traditional experimental and computational methods.

This review aims to outline recent progress of ML-assisted 2D materials growth, including optimizing synthesis conditions, automating real-time characterizations and unveiling growth mechanisms. Current challenges and future prospects in this frontier research field are also discussed. Overall, this review highlights the synergy between advanced ML techniques and surface growth approaches for accelerated materials synthesis and intelligent design of next-generation functional devices.

This work received support from the National Key Research and Development Program, the National Natural Science Foundation of China, and the Zhejiang Provincial Natural Science Foundation. The paper's first author is Gao Wenjin, a doctoral candidate in the School of Physics, and the corresponding author is Professor Zhou Miao. Beihang University is the primary affiliation for the study.

Link to the article: https://www.sciencedirect.com/science/article/pii/S0167572926000014

Editor: Lyu Xingyun