In this study, we investigate the application of machine learning to XPS verification, focusing on spectral peak identification. We compare the performance of different machine learning models, including neural networks, support vector machines, and random forests, on a dataset of XPS spectra from various materials.
However, XPS spectra often suffer from peak overlapping, where multiple peaks from different elements or chemical states overlap, making it difficult to accurately identify and quantify the peaks. Additionally, noise and instrumental broadening can further complicate the analysis. xpsverification.com
The application of machine learning to XPS verification offers several advantages over traditional methods. Firstly, machine learning models can automate the peak identification process, reducing the need for manual analysis and minimizing the risk of human error. Secondly, machine learning models can handle large datasets and identify patterns that may not be apparent to human analysts. In this study, we investigate the application of