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

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

doi:10.1038/s41524-020-00472-7

Abstract

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.

Highlights

Materials scientists investigate or characterize engineering materials by analyzing a series of micrographs that reveal its complex microstructure at scales varying from the millimeter down to the nanometer
As a core component of deep learning for image recognition, convolutional neural networks (CNNs) have the potential ability to speed up the analysis of micrographs and improve the repeatability of the analysis, and have the potential to reveal unforeseen patterns and details that would be hidden without application of advanced data-mining techniques[2,4]
The most important is that the traditional method is only based on the differences in compositions, while the present method attempts to take into account the entire crystal structure information including the occupancy sites and types of different atoms, more exactly, how this crystallographic information manifests its signature in 2D zxSDM images

Summary

INTRODUCTION

Materials scientists investigate or characterize engineering materials by analyzing a series of micrographs that reveal its complex microstructure at scales varying from the millimeter down to the nanometer. Machine learning algorithms have the potential to unveil ordered structures by learning characteristic patterns in experimentally obtained SDMs. As a representative in the field of image recognition, CNNs have been used to automate the identification of microstructural and crystallographic features using micrographs[4,24,25]. A CNN-based strategy is proposed to automatically recognize nanoscale L12-type ordered structures in FCC-based alloys using APT data with an ultra-high recognition ability. A crystal structure library was built to include a wide range of possible configurations to feed into producing many simulations of APT data, all based on either the L12 or FCC crystal structure From these simulated structures, the corresponding zxSDMs along with specific crystallographic direction were generated. The rotation augmentation was applied to simulate the observed small-angle

RESULTS

DISCUSSION

METHODS