Scientists propose machine learning method for 2-D material spectroscopy

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Basic architecture of the learning procedure in the random forest method. Credit: SIOM

Machine learning is an important branch in the field of artificial intelligence. Its basic idea is to build a statistical model based on data and use the model to analyze and predict the data. With the rapid development of big data technology, data-driven machine learning algorithms have begun to flourish in various fields of materials research.

Recently, a research team led by Prof. Wang Jun from the Shanghai Institute of Optics and Fine Mechanics of the Chinese Academy of Sciences (CAS) proposed a recognition method to distinguish the monolayer continuous film and random defect areas of two-dimensional (2-D) semiconductors using the machine learning method with Raman signals.
Their work revealed the application potential of machine learning algorithms in the field of 2-D material spectroscopy, and was published in Nanomaterials.
The Raman spectrum of 2-D materials is very sensitive to molecular bonding and sample structure, and can be used for research and analysis of chemical identification, morphology and phase, internal pressure/stress, and composition. Although Raman spectroscopy provides enough information, how to mine the depth of information and use multiple information to make comprehensive decisions still needs further …

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