Direct prediction of inelastic neutron scattering spectra from the crystal structure
Direct prediction of inelastic neutron scattering spectra from the crystal structure
复制标题
从晶体结构直接预测非弹性中子散射谱
DOI：
10.1088/2632-2153/acb315
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
A. Ramirez‐Cuesta
中科院分区：
文献类型：
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作者：
Yongqiang Cheng;Geoffrey Wu;D. Pajerowski;M. Stone;A. Savici;Mingda Li;A. Ramirez‐Cuesta
非弹性中子散射（INS）是研究材料振动动力学的一种强有力的技术，具有许多独特的优点。然而，INS光谱的分析和解释通常需要高级建模，需要专门的计算资源和相关专业知识。进行国际惯性系统测量的实验资源有限，使这一困难更加复杂。在这项工作中，我们开发了一个基于机器学习的预测框架，该框架能够直接预测一维INS光谱和二维INS光谱，并具有额外的动量分辨率。通过将对称感知神经网络与自编码器相结合，利用大规模合成INS数据库，将高维光谱数据压缩为潜在空间表示，仅使用原子坐标作为输入即可实现高质量的光谱预测。我们的工作提供了直接从简单输入预测复杂多维中子谱的有效方法；它可以提高使用有限的INS测量资源的效率，并在各种动态实验数据分析场景中阐明构建结构-属性关系。
Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of materials with several unique advantages. However, analysis and interpretation of INS spectra often require advanced modeling that needs specialized computing resources and relevant expertise. This difficulty is compounded by the limited experimental resources available to perform INS measurements. In this work, we develop a machine-learning based predictive framework which is capable of directly predicting both one-dimensional INS spectra and two-dimensional INS spectra with additional momentum resolution. By integrating symmetry-aware neural networks with autoencoders, and using a large scale synthetic INS database, high-dimensional spectral data are compressed into a latent-space representation, and a high-quality spectra prediction is achieved by using only atomic coordinates as input. Our work offers an efficient approach to predict complex multi-dimensional neutron spectra directly from simple input; it allows for improved efficiency in using the limited INS measurement resources, and sheds light on building structure-property relationships in a variety of on-the-fly experimental data analysis scenarios.