论文标题
通过贝叶斯分析原子解析的茎数据探索铁电域壁的物理
Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
论文作者
论文摘要
使用原子解析的茎数据的贝叶斯推断分析探索了铁电域壁的物理学。我们证明，域壁轮廓形状最终对材料中顺序参数的性质敏感，包括Ginzburg-Landau-Devonshire扩展的功能形式，以及相应参数的数值。先前分布的形式自然会在贝叶斯框架中自然折叠材料知识，并以不同的顺序参数形成竞争（或层次结构）模型。在这里，我们使用此方法探索了BifeO3中铁电域壁的物理，并得出相关参数的后验估计。更一般而言，这种推论方法既可以从具有相关的不确定性量化的实验数据中学习材料物理学，又可以为工具开发建立指南，以回答有关哪些解决方案和信息限制的问题，以可靠地观察特定的物理感兴趣的物理机制。
The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ultimately sensitive to the nature of the order parameter in the material, including the functional form of Ginzburg-Landau-Devonshire expansion, and numerical value of the corresponding parameters. The preexisting materials knowledge naturally folds in the Bayesian framework in the form of prior distributions, with the different order parameters forming competing (or hierarchical) models. Here, we explore the physics of the ferroelectric domain walls in BiFeO3 using this method, and derive the posterior estimates of relevant parameters. More generally, this inference approach both allows learning materials physics from experimental data with associated uncertainty quantification, and establishing guidelines for instrumental development answering questions on what resolution and information limits are necessary for reliable observation of specific physical mechanisms of interest.
