相变固体化学力学的相关图像学习
Correlative image learning of chemo-mechanics in phase-transforming solids
摘要：
本构律是自然界大多数物理过程的基础。然而，在非均匀固体中学习这样的方程（例如，由于相分离）具有挑战性。其中一种关系是组成和本征应变之间的关系，它决定了固体中的化学机械膨胀。在这项工作中，我们开发了一个可推广的，物理约束的图像学习框架，从相关的四维扫描透射电子显微镜和X射线光谱ptychography图像中算法学习纳米级的化学力学本构关系。我们在Li $_X$ FePO $_4$（一种技术相关的电池正极材料）上证明了这种方法。我们揭示了在这个两相二元固体在整个组成范围（0 $\leq$ X $\leq$ 1），包括内部的结晶不稳定的可伸缩间隙的组成-本征应变关系的函数形式。所学到的关系直接验证了Vegard在纳米尺度下的线性响应定律。我们的物理约束的数据驱动的方法直接可视化的残余应变场（通过删除组成和相干应变），这是不可能量化。在残余应变的异质性产生的失配位错，并通过X射线衍射线轮廓分析进行了独立验证。我们的工作提供了同时量化电池电极中的化学膨胀、相干应变和位错的方法，这对速率能力和寿命有影响。从广义上讲，这项工作还突出了整合相关显微镜和图像学习来提取材料特性和物理特性的潜力。
英文摘要：
Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (e.g., due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. In this work, we developed a generalizable, physically-constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on Li$_X$FePO$_4$, a technologically-relevant battery positive electrode material. We uncovered the functional form of composition-eigenstrain relation in this two-phase binary solid across the entire composition range (0 $\leq$ X $\leq$ 1), including inside the thermodynamically-unstable miscibility gap. The learned relation directly validates Vegard's law of linear response at the nanoscale. Our physics-constrained data-driven approach directly visualizes the residual strain field (by removing the compositional and coherency strain), which is otherwise impossible to quantify. Heterogeneities in the residual strain arise from misfit dislocations and were independently verified by X-ray diffraction line profile analysis. Our work provides the means to simultaneously quantify chemical expansion, coherency strain and dislocations in battery electrodes, which has implications on rate capabilities and lifetime. Broadly, this work also highlights the potential of integrating correlative microscopy and image learning for extracting material properties and physics.
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