EELSpecNet is a Python-based deep convolutional neural network designed for tackling challenges in electron energy loss spectroscopy (EELS) spectral deconvolution. EELS is a powerful technique for studying the chemical and electronic properties of materials at the nanometer length-scale. It is capable of performing near-meV energy-resolution spectroscopy, exploring plasmonic and phononic activities, and measuring energy gains. However, the output spectra often suffer from high-frequency noise and convolution with the optical transfer function (OTF). EELSpecNet offers a solution to this problem by implementing a blind deconvolutional neural network architecture inspired by the U-shaped and dilated deep neural network architectures.
The training process needs to be closely monitored and can be computationally expensive.
EELSpecNet is a Python script. The specific Python version and libraries required will be specified in the ‘Installation’ section.
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For any issues, bugs, feature requests, or questions about EELSpecNet, please open an issue in the issue tracker, or contact the authors directly.
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