Samarakoon Anjana M, Barros Kipton, Li Ying Wai, Eisenbach Markus, Zhang Qiang, Ye Feng, Sharma V, Dun Z L, Zhou Haidong, Grigera Santiago A, Batista Cristian D, Tennant D Alan
Neutron Scattering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
Theoretical Division and CNLS, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Nat Commun. 2020 Feb 14;11(1):892. doi: 10.1038/s41467-020-14660-y.
Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like DyTiO. Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use an automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in DyTiO. The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems.
复杂行为在从实验中提取模型方面带来了挑战。一个例子是在诸如DyTiO等受挫磁体中形成自旋液体。包括无序、玻璃态形成以及散射数据解释等问题阻碍了人们的理解。在此，我们利用一种自动化能力从数据中提取模型哈密顿量，并识别不同的磁态。这涉及训练一个自动编码器，以在广泛的自旋哈密顿量范围内学习三维漫散射的压缩表示。自动编码器根据散射和热容量数据找到最佳匹配，并提供置信区间。验证测试表明，我们的最优哈密顿量能够准确预测磁结构和磁化强度的温度及场依赖性，以及DyTiO中的玻璃态形成和不可逆性。自动编码器还可以对不同的磁行为进行分类，并消除原始数据中的背景噪声和伪像。我们的方法很容易应用于其他材料和类型的散射问题。
