AI model helps understand defects in polycrystalline materials

Researchers at Nagoya University in Japan used artificial intelligence (AI) to discover a new method for understanding small defects called dislocations in polycrystalline materials, which are widely used in information equipment, solar cells and electronic devices. Dislocations can reduce the efficiency of these devices. The researchers report their findings in a paper in Advanced Materials.

Almost every device that we use in our modern lives has a polycrystalline component: from smartphones to computers to cars. Despite this, polycrystalline materials are tough to utilize because of their complex microstructures, which can affect their performance.

A major problem with using polycrystals in industry is the formation of tiny crystal defects caused by stress and temperature changes. These are known as dislocations and can disrupt the regular arrangement of atoms in the crystal lattice, affecting electrical conduction and overall performance. To reduce the chances of failure in devices that use polycrystalline materials, it is important to understand the formation of these dislocations. 

A team of researchers at Nagoya University in Japan, led by Noritaka Usami, has now used a new AI tool to analyze image data of a material called polycrystalline silicon, which is widely used in solar panels. The AI tool created a 3D model of polycrystalline silicon in virtual space, helping the team to identify areas where dislocation clusters were affecting the material’s performance.

After identifying areas with dislocation clusters, the researchers used electron microscopy and theoretical calculations to understand how these areas formed. This revealed stress distribution in the crystal lattice and staircase-like structures at the boundaries between the crystal grains. These structures appear to cause dislocations during crystal growth.

“We found a special nanostructure in the crystals associated with dislocations in polycrystalline structures,” Usami said.

Along with its practical implications, this study may have important implications for the science of crystal growth and deformation. The Haasen-Alexander-Sumino (HAS) model is an influential theoretical framework used to understand the behavior of dislocations in materials. But Usami believes they have discovered dislocations that are missed by the Haasen-Alexander-Sumino model.

Another surprise came when the team calculated the arrangement of the atoms in these structures. They unexpectedly discovered large tensile bond strains along the edge of the staircase-like structures that triggered the generation of dislocations.

“As experts who have been studying this for years, we were amazed and excited to finally see proof of the presence of dislocations in these structures,” said Usami. “It suggests that we can control the formation of dislocation clusters by controlling the direction in which the boundary spreads.

“By extracting and analyzing the nanoscale regions through polycrystalline materials informatics, which combines experiment, theory and AI, we made this clarification of phenomena in complex polycrystalline materials possible for the first time. This research illuminates the path towards establishing universal guidelines for high-performance materials and is expected to contribute to the creation of innovative polycrystalline materials. The potential impact of this research extends beyond solar cells to everything from ceramics to semiconductors. Polycrystalline materials are widely used in society, and the improved performance of these materials has the potential to revolutionize society.”

This story is adapted from material from Nagoya University, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.