npj: 卷积神经网络计算—精确识别纳米级有序结构

Posted 2021-04-03 知社学术圈

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目前的材料科学家一般通过分析一系列显微照片来研究或描述工程材料的特性，包括从毫米到纳米的复杂微观结构。这些工作通常是由科学家个人手动完成的，有时还需要计算技术的辅助。这些以人为中心的工作流程存在严重的缺点，如对专业要求高、可重复性差、过程耗时长等。以纳米级L12型有序结构为例，该结构被广泛用于面心立方（FCC）合金中，以利用其硬化能力，从而提高机械性能。这些细尺度的颗粒通常与具有相同原子构型、不考虑化学种类的基体完全相干，这使得他们的表征具有挑战性。空间分布图（SDMs）用于通过询问重建原子探针断层扫描（APT）数据内原子的三维（3D）分布来探究局部秩序。然而，手动分析完整的点云（> 1000万个）以寻找数据中保留的部分晶体学信息，几乎是不可能的。

来自德国马普所的Yue Li和Leigh T. Stephenson等提出了一种基于卷积神经网络(CNNs)的策略，利用APT数据自动识别FCC基合金中的纳米级L12型有序结构，具有超高的识别能力。该方法首先生成了模拟L12有序结构的SDMs和FCC矩阵。这些模拟图像结合少量的实验数据，用于训练基于CNN的L12有序结构识别模型。最后，成功应用该方法揭示了FCC Al-Li-Mg体系中平均半径为2.54 nm的L12型δ'-Al3（LiMg）纳米颗粒的3D分布。可检测得纳米域最小半径甚至低至5 Å。所提出的CNN-APT方法很有希望在不久的将来扩展到识别其他纳米级的有序结构，甚至更有挑战性的短程有序现象中。

该文近期发表于npj Computational Materials 7: 8 (2021)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

Yue Li, Xuyang Zhou, Timoteo Colnaghi, Ye Wei, Andreas Marek, Hongxiang Li, Stefan Bauer, Markus Rampp & Leigh T. Stephenson 

Nanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.

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