Spectroscopic techniques like X-ray diffraction (XRD), Nuclear Magnetic Resonance (NMR), and Raman spectroscopy are vital for characterizing materials. Each produces one-dimensional spectra with peaks indicating molecular or crystalline structure. Manual analysis is challenging due to experimental artifacts and variations. Machine learning, particularly artificial neural networks (ANNs), offers automation. ANNs, mimicking the human brain, process spectra to identify structures. Convolutional Neural Networks (CNNs) are particularly effective at handling artifacts, extracting relevant peak information. While CNNs have shown success in various spectroscopic applications, individual studies often employ unique architectures without comparing their effectiveness. Existing large spectroscopic databases are limited in size and scope, don't fully represent experimental variation, and are difficult to curate. To address this, a universal synthetic dataset is proposed for robust model validation, encompassing features common to XRD, Raman, and NMR, and allowing manipulation of experimental artifacts.

Previous research demonstrates the application of CNNs for spectroscopic data classification, achieving high accuracies in different domains such as XRD and Raman spectroscopy. However, these studies often used unique architectures and datasets, hindering direct comparison and understanding of general limitations. The accuracies reported vary significantly, making it unclear if this disparity stems from architectural differences or inherent task difficulty. Advanced deep learning techniques, such as VGG networks, Batch Normalization, and residual or inception blocks, have shown promise in image processing, but their effectiveness in spectroscopic data classification needs further investigation. The lack of a standardized benchmark dataset limits our understanding of generalizability and the comparative performance of diverse neural network architectures.

A synthetic dataset comprising 500 distinct classes was created, each representing a unique material. Each class contains 60 spectra (50 for training, 10 for validation), generated stochastically with variations in peak positions, intensities, and widths, mimicking experimental artifacts. Eight pre-existing neural network architectures—three basic CNNs (CNN2, CNN3, CNN6), two with inception blocks (INC3, INC6), a VGG network, a CNN with Batch Normalization (CNN BN), and a ResNet—were selected and trained on this dataset. The models were trained five times each with different random weight initializations. Accuracy was measured on a separate test set with uniformly distributed variations. A secondary, "challenging" dataset was created with significant peak overlap to further probe model robustness. Models were re-trained on this dataset, both with linear and ReLU activation functions in the fully connected layers. The impact of Batch Normalization, dropout, and model complexity was also investigated.

All eight models achieved high accuracy (≥98.9%) on the general synthetic dataset, suggesting that spectral classification may be less challenging than image classification. However, performance varied on the more challenging dataset. The CNN6 model consistently outperformed others, primarily due to the use of ReLU activation functions between fully connected layers, enabling the learning of multiple thresholds to distinguish classes with overlapping peaks. More complex architectures (VGG, ResNet, Inception) did not provide significant performance improvements. Misclassifications primarily stemmed from three factors: (1) overlooking minor peaks, (2) overlapping peak positions, and (3) overlapping peak intensities. Batch Normalization caused unstable training, and dropout placement significantly influenced performance. Removing Batch Normalization or adjusting dropout placement in some models significantly improved their performance on the challenging dataset. The CNN6 model, even with its limitations, remains the best performer due to its use of ReLU activations and appropriate regularization.

This study highlights that while many neural network architectures perform well on relatively simple spectroscopic datasets, performance varies significantly on more complex datasets with overlapping features. The use of ReLU activation functions in the fully connected layers is crucial for accurate classification of spectra with overlapping features. The results suggest that simpler architectures, like CNN6, are better suited for spectroscopic data classification than more complex models (VGG, ResNet, Inception). The impact of regularization and the location of dropout layers demonstrates the importance of careful model selection and hyperparameter tuning. The proposed synthetic dataset provides a valuable tool for validating machine learning models for spectroscopic data, allowing for controlled experimentation and comprehensive performance evaluation across different model architectures. Future work could focus on developing networks that better handle minor peaks and overlapping peak positions and intensities, exploring different regularization methods, and expanding the synthetic dataset to incorporate more complex spectral characteristics.

This work presents a universal synthetic dataset and a comprehensive benchmark of eight neural network architectures for spectroscopic data classification. The findings underscore the importance of ReLU activation functions and careful regularization for handling overlapping spectral features. Simpler architectures like CNN6 show superior performance compared to more complex models. The publicly available dataset and code provide a valuable resource for advancing the field. Future research should focus on improving models' ability to discern minor peaks and address the limitations identified in this study, potentially through novel architectural designs or enhanced data augmentation techniques.

The synthetic dataset, while comprehensive, simplifies certain aspects of real-world spectroscopic data. It uses idealized Gaussian peak shapes and does not include background noise or other complexities. This simplification could limit the generalizability of the findings to real-world datasets. The focus on single-phase spectra might not fully reflect the challenges of analyzing multi-phase mixtures. The specific choices of hyperparameters and training strategies used here might also influence the observed results. Further research should investigate the effect of these parameters and explore broader optimization techniques.