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

作　　者：Yikun Li Lunyang Liu Xiaoning Zhao Shuming Zhou Xuehui Wu Yuecheng Lai Zhongjun Chen Jizhong Chen Xueqing Xing 

机构地区：[1]School of Chemical Engineering and Light Industry,Guangdong University of Technology,Guangzhou,510006,China [2]Institute of High Energy Physics,Chinese Academy of Sciences,Beijing,100049,China [3]State Key Laboratory of Polymer Physics and Chemistry,Changchun Institute of Applied Chemistry,Chinese Academy of Sciences,Changchun,130022,China [4]Beijing Institute of Metrology,Beijing,100029,China [5]University of Chinese Academy of Sciences,Beijing,100049,China

出　　处：《Radiation Detection Technology and Methods》2024年第4期1712-1728,共17页辐射探测技术与方法(英文)

基　　金：supported by the Innovation Program of the Institute of High Energy Physics,CAS(Grant Number 2023000034);the National Natural Science Foundation of China[Grant Numbers 22273013,12275300);National Key R&D Program of China(Grant Numbers 2022YFA1603802,2017YFA0403000).

摘　　要：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.

关 键 词：SAXS Nanoparticles growth process In situ Machine learning CNN Shape classification Size predication 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]