A Survey of Artificial Intelligence Applications Throughout the Full Lifecycle of Neutron Scattering Experiments

doi:10.19678/j.issn.1000-3428.0069651

Abstract

Abstract:

This study explores the use of Artificial Intelligence (AI) technology throughout the neutron scattering experiments′ lifecycle to determine how AI technology can revolutionize key aspects such as experimental apparatus, data acquisition, and data processing. The study begins by introducing the fundamental principles and experimental procedures of neutron scattering technology before focusing on the multifaceted applications of AI technology in neutron scattering experiments. These applications include optimizing experimental infrastructure, data acquisition, and imaging preprocessing, as well as characterizing experimental samples in neutron diffraction, neutron reflection, and Inelastic Neutron Scattering (INS). This study demonstrates the importance of AI technology in increasing the intelligence level of experiments, accelerating data processing, and improving the accuracy and reliability of data analyses. In addition, an in-depth discussion is held on the future application of AI technology in neutron scattering experiments, indicating that with the continuous advancement of technologies such as multimodal learning, interpretable models, large language models, and AI-Ready databases, AI technology is poised to bring revolutionary changes to neutron scattering experiments, opening up new avenues for revealing the microstructure and properties of complex material systems.

Key words: Artificial Intelligence (AI), neutron scattering, neural network, multimodal learning, sample characterization

摘要：

探讨人工智能(AI)技术在中子散射实验全生命周期中的应用，旨在梳理AI技术如何革新中子散射实验装置、数据采集、数据处理等关键环节。首先介绍中子散射技术的基本原理和实验流程，然后重点讨论AI技术在中子散射实验中的多方面应用，包括实验基础设施的优化设计、数据采集与成像的数据预处理以及中子衍射、中子反射、非弹性中子散射(INS)等实验样品表征方面的应用，展示AI技术在提高实验的智能化水平、加快数据处理速度、提升数据分析的准确性和可靠性等方面的重要性。此外，对AI技术在中子散射实验中的未来应用进行深入讨论，指出随着多模态学习、可解释模型、大语言模型、AI-Ready数据库等技术的不断进步和应用领域的拓展，AI技术有望为中子散射实验带来革命性的变革，为揭示复杂物质系统的微观结构和性质开辟新的途径。

关键词: 人工智能, 中子散射, 神经网络, 多模态学习, 样品表征

LI Yakang, LI Jianfang, HU Peng, CHEN Juan, WANG Shengxiang, QI Fazhi, CHEN Gang. A Survey of Artificial Intelligence Applications Throughout the Full Lifecycle of Neutron Scattering Experiments[J]. Computer Engineering, 2025, 51(10): 53-70.

李亚康, 李健芳, 胡鹏, 陈娟, 王声翔, 齐法制, 陈刚. 人工智能在中子散射实验全生命周期中的应用综述[J]. 计算机工程, 2025, 51(10): 53-70.

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Figures/Tables 15

Fig.1 Improvement of neutron source

Fig.2 Process of neutron scattering experiment

Fig.3 Network architecture of U-Net

Fig.4 Network architecture of VAE

Fig.5 Architecture of GAN

Fig.6 GNN for crystal structures

Fig.7 Process of data-driven Q-point sampling

Fig.8 Comparison of test results for image reconstruction based on FBP, SIRT, and NN-FBP

Fig.9 Binary-tree in memory metadata index structure based on NX class types

Fig.10 Schematic diagram of LC-MS peak detection algorithm

Fig.11 Schematic diagram of the SRCNN-POCS algorithm

Fig.12 2.5D AI-CT model and MSD-Net model for neutron image reconstruction

Fig.13 Neural network structure with six hidden layers

Fig.14 The process of encoding crystal structures and inputting into GNN

Fig.15 Workflow of ML-SCS combining scattering experiments with theory to extract model parameters and phase diagram information

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