Machine Learning for Characterization and Analysis of Microstructure and Spectral Data of Materials


  • Venkataramaiah Gude, Sujeeth T, K Sree Divya, P. Dileep Kumar Reddy, G. Ramesh


Nano Material Characterization, STEM Analysis, Spectral Data Analysis, Machine Learning, Microstructure Analysis


In the contemporary world, there is lot of research going on in creating novel nano materials that are essential for many industries including electronic chips and storage devices in cloud to mention few. At the same time, there is emergence of usage of machine learning (ML) for solving problems in different industries such as manufacturing, physics and chemical engineering. ML has potential to solve many real world problems with its ability to learn in either supervised or unsupervised means. It is inferred from the state of the art that that it is essential to use ML methods for analysing imagery of nano materials so as to ascertain facts further towards characterization and analysis of microstructure and spectral data of materials. Towards this end, in this paper, we proposed a ML based methodology for STEM image analysis and spectral data analysis from STEM image of a nano material. We proposed an algorithm named Machine Learning for STEM Image Analysis (ML-SIA) for analysing STEM image of a nano material. We proposed another algorithm named Machine Learning for STEM Image Spectral Data Analysis (ML-SISDA) for analysing spectral data of STEM image of a nano material. We developed a prototype ML application to implement the algorithms and evaluate the proposed methodology. Experimental results revealed that the ML based approaches are useful for characterization of nano materials. Thus this research helps in taking this forward by triggering further work in the area of material analysis with artificial intelligence.


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How to Cite

Venkataramaiah Gude, Sujeeth T, K Sree Divya, P. Dileep Kumar Reddy, G. Ramesh. (2024). Machine Learning for Characterization and Analysis of Microstructure and Spectral Data of Materials. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 820–826. Retrieved from



Research Article