论文标题
使用可解释的图神经网络范式一次量化一个原子
Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm
论文作者
论文摘要
量化材料内原子障碍的水平对于理解局部结构环境如何决定性能和耐用性至关重要。在这里，我们利用图形神经网络定义了局部疾病的物理解释度量。该度量标准将局部原子构构的多样性编码为固体和液相之间的连续光谱，并针对热扰动的分布进行了量化。我们将这种新颖的方法应用于三个具有不同障碍水平的原型示例：（1）固液界面，（2）多晶微观结构和（3）晶粒边界。使用元素铝作为案例研究，我们展示了我们的范式如何跟踪接口的时空演化，并结合了对阶和混乱之间空间边界的数学定义描述。我们进一步展示了如何从我们的连续障碍场中提取物理保存的梯度，这些梯度可用于理解和预测材料的性能和失败。总体而言，我们的框架提供了一种直观且可推广的途径，以量化复杂的局部原子结构与粗粒物质现象之间的关系。
Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this novel methodology to three prototypical examples with varying levels of disorder: (1) solid-liquid interfaces, (2) polycrystalline microstructures, and (3) grain boundaries. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides an intuitive and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.
