This article focuses on applying machine learning to efficiently identify and characterize the thickness of two-dimensional (2D) materials using optical microscopy images. Traditional methods, like atomic force microscopy and spectroscopy, are precise but expensive and time-consuming. To address this, the study evaluates the performance of SegNet, 2D U-Net, and 1D U-Net machine learning models for identifying monolayers in optical images. The research incorporates preprocessing techniques, such as grayscale and Lab* color space transformations, and data augmentation to improve accuracy. Among the models, 2D U-Net demonstrates the best performance, especially when using normalized image preprocessing and transfer learning with pre-labeled datasets. This approach highlights the potential for machine learning to provide a fast, cost-effective solution for 2D material characterization, promoting broader use of these materials in electronics and quantum technologies.
For more details, please continue reading the full article under the following link:
https://www.nature.com/articles/s41598-024-79386-z
In general, if you enjoy reading this kind of scientific news articles, I am always keen to connect with fellow researchers in materials science, including the possibility to discuss about any potential interest in the Materials Square cloud-based online platform ( www.matsq.com ), designed for streamlining the execution of materials and molecular modelling simulations!
The Materials Square platform in fact provides extensive web-based functionalities for computational chemistry/materials research and training/education purposes, as detailed also in the following PDF brochure: https://www.materialssquare.com/wp-content/uploads/Materials_Square_Brochure_2022-compressed_1674181754.pdf
Many thanks for your interest and consideration,
Dr. Gabriele Mogni
Technical Consultant and EU Representative of Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
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