可预测性作为显性与隐性物理的探针：多铁性Sm掺杂BiFeO3中原子尺度结构、化学与极化行为的案例研究
Predictability as a probe of manifest and latent physics: The case of atomic scale structural, chemical, and polarization behaviors in multiferroic Sm-doped BiFeO3
AI总结：
本文利用高斯过程回归分析Sm掺杂BiFeO3的原子分辨率STEM多模态观测数据，通过不同描述符组合对局部极化场的可预测性及不确定性，揭示畴壁和界面等微结构背后的物理机制，提出以可预测性作为探测凝聚态系统物理的新方法。
摘要：
某一效应或现象的可预测性通常等同于对相关物理定律的掌握，通常被理解为观测值与系统已知状态之间的函数关系或数值推导关系。相应地，与先验知识不一致的观测结果可用于推导关于系统性质的新知识，或表明存在尚未知晓的机制。本文探索高斯过程（GP）在从多模态观测中建立局部行为的可预测性与不确定性方面的适用性，为这一经典范式提供一种替代方案。利用多铁性Sm掺杂BiFeO3在宽成分范围内的原子分辨率扫描透射电子显微镜（STEM），我们直接可视化了该材料的原子结构以及结构、物理和化学序参量场。使用GP回归建立局部极化场相对于不同参数组的可预测性，这些参数包括相邻极化值以及若干物理和化学描述符的组合，包括晶格参数、柱强度等。我们观察到，微结构的某些元素，包括带电和不带电畴壁以及与衬底的界面，能够通过特定的描述符组合得到最佳预测，并且这种可预测性及其相关不确定性在整个成分系列中保持一致。文中讨论了相关的生成性物理机制。我们认为，观测数据中的可预测性和不确定性为从多模态局部观测中探测凝聚态物质系统的物理提供了一条新途径。
英文摘要：
The predictability of a certain effect or phenomenon is often equated with the knowledge of relevant physical laws, typically understood as a functional or numerically derived relationship between the observations and known states of the system. Correspondingly, observations inconsistent with prior knowledge can be used to derive new knowledge on the nature of the system or indicate the presence of yet unknown mechanisms. Here we explore the applicability of Gaussian Processes (GP) to establish predictability and uncertainty of local behaviors from multimodal observations, providing an alternative to this classical paradigm. Using atomic-resolution Scanning Transmission Electron Microscopy (STEM) of multiferroic Sm-doped BiFeO3 across a broad composition range, we directly visualize the atomic structure and structural, physical, and chemical order parameter fields for the material. GP regression is used to establish the predictability of the local polarization field from different groups of parameters, including the adjacent polarization values and several combinations of physical and chemical descriptors, including lattice parameters, column intensities, etc. We observe that certain elements of microstructure including charged and uncharged domain walls and interfaces with the substrate are best predicted with specific combinations of descriptors, and this predictability and their associated uncertainties are consistent across the composition series. The associated generative physical mechanisms are discussed. We argue that predictability and uncertainty in observational data offers a new pathway to probe the physics of condensed matter systems from multimodal local observations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
