Deep learning-assisted characterization of nanoparticle growth processes: unveiling SAXS structure evolution

Purpose The purpose of this study is to explore deep learning methods for processing high-throughput small-angle X-ray scattering (SAXS) experimental data.
Methods The deep learning algorithm was trained and validated using simulated SAXS data, which were generated in batches based on the theoretical SAXS formula using Python code. Our self-developed SAXSNET, a convolutional neural network based on PyTorch, was employed to classify SAXS data for various shapes of nanoparticles. Additionally, we conducted comparative analysis of classification algorithms including ResNet-18, ResNet-34 and Vision Transformer. Random Forest and XGboost regression algorithms were used for the nanoparticle size prediction. Finally, we evaluated the aforementioned shape classification and numerical regression methods using actual experimental data. A pipeline segment is established for the processing of SAXS data, incorporating deep learning classification algorithms and numerical regression algorithms.
Results After being trained with simulated data, the four deep learning algorithms achieved a prediction accuracy of over 96% on the validation set. The fine-tuned deep learning model demonstrated robust generalization capabilities for predicting the shapes of experimental data, enabling rapid and accurate identification of morphological changes in nanoparticles during experiments. The Random Forest and XGboost regression algorithms can simultaneously provide faster and more accurate predictions of nanoparticle size.
Conclusion The pipeline segment constructed in this study, integrating deep learning classification and regression algorithms, enables real-time processing of high-throughput SAXS data. It aims to effectively mitigates the impact of human factors on data processing results and enhances the standardization, automation, and intelligence of synchrotron radiation experiments.