从中子粉末衍射数据机器学习磁性相互作用
Machine learning magnetic interactions from neutron powder diffraction data
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中文总结 AI 辅助
本文研究利用机器学习从粉末中子衍射的磁漫散射数据预测磁性相互作用参数，在八种高对称晶格上达到约2%的高精度，避免了非线性最小二乘的假极小问题，表明粉末漫散射数据可作为磁性相互作用的紧凑指纹。
中文摘要 AI 辅助
中子衍射是一种多功能的实验技术，能够探测材料的磁性质。虽然衍射通常用于确定材料的磁结构，但衍射实验中的磁漫散射数据也对哈密顿量中的磁性相互作用敏感。然而，从中子散射数据准确确定磁性相互作用参数涉及一个逆散射问题，该问题通常难以求解。在此，我们研究了一种机器学习方法在给定粉末样品上测量的磁漫散射数据预测相互作用参数的有效性，针对八种高对称晶格上的各向同性相互作用进行了全面调查。在我们考虑的所有晶格中，机器学习方法以高（约2%）精度估计相互作用参数，同时避免了非线性最小二乘精修中遇到的假极小问题。我们的结果强调，粉末漫散射数据可以为许多材料提供磁性相互作用的紧凑“指纹”。
英文摘要
Neutron diffraction is a versatile experimental technique capable of probing a material's magnetic properties. While diffraction is typically used to determine the magnetic structure of a material, magnetic diffuse scattering data from a diffraction experiment are also sensitive to the magnetic interactions in its Hamiltonian. However, accurately determining magnetic interaction parameters from neutron-scattering data involves an inverse scattering problem that is challenging to solve in general. Here, we investigate the effectiveness of a machine learning approach to predict the interaction parameters given magnetic diffuse-scattering data measured on powder samples, for a comprehensive survey of isotropic interactions on eight high-symmetry lattices. Across all lattices we considered, the machine-learning approach estimates the interaction parameters with high (~2%) accuracy, while avoiding the issue of false minima that is encountered with non-linear least squares refinement. Our results highlight that powder diffuse-scattering data can provide a compact "fingerprint" of the magnetic interactions for many materials.
发表机构
- School of Physics, Georgia Institute of Technology（佐治亚理工学院物理学院）
- Neutron Scattering Division, Oak Ridge National Laboratory（橡树岭国家实验室中子散射部）
- Materials Science and Technology Division, Oak Ridge National Laboratory（橡树岭国家实验室材料科学与技术部）
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