Scientists have made significant progress in understanding the secrets of two-dimensional (2D) materials through the use of artificial intelligence (AI) and scanning tunneling microscopy (STM). These ultra-thin materials, consisting of only a few atoms, possess unique properties governed by quantum mechanics. However, it is their defects that often contribute to their special characteristics. Unlocking the potential of these defects has presented a challenge to researchers due to the countless possibilities and complexities involved.
In order to tackle this challenge, a team of scientists developed an automated method that combines AI, STM, and Molecular Foundry – a user facility of the Department of Energy Office of Science. The technique allows for the analysis of how matter interacts with electromagnetic radiation, which is a crucial aspect in understanding the behavior of 2D materials. By leveraging AI and machine learning (ML), the researchers were able to perform spatially dense, point spectroscopic measurements using STM, resulting in faster and more accurate data. This data mapping and identification process greatly aids in the recognition of spectroscopic signatures of various heterogeneous surfaces.
To demonstrate the effectiveness of their approach, the team focused on two materials: tungsten disulfide (WS2) and gold (Au-111). By performing reproducible measurements, the researchers were able to create statistically significant electronic structure characterizations of the different intrinsic defects found on these materials. This breakthrough has the potential to greatly enhance our understanding of 2D materials and their applications in various fields.
The research was supported by the Center for Novel Pathways to Quantum Coherence in Materials, an Energy Frontier Research Center funded by the Department of Energy Office of Science. Additionally, funding was provided through the Center for Advanced Mathematics for Energy Research Applications, which is jointly funded by the DOE Office of Science’s Advanced Scientific Computing Research and Basic Energy Sciences programs. The National Science Foundation, Division of Materials Research, and the Swiss National Science Foundation also contributed to the project.
This latest development in the study of 2D materials has significant implications for a wide range of fields, including electronics, energy storage, and catalysis. The ability to accurately analyze and understand the defects in these materials will enable researchers to harness their unique properties and tailor them for specific applications. By combining AI, STM, and ML, scientists have taken a crucial step forward in unlocking the secrets of 2D materials and paving the way for innovative technological advancements.
Overall, this research highlights the power of combining cutting-edge technologies with scientific expertise to unravel the mysteries of the microscopic world. The use of AI and STM in conjunction with ML has proven to be a highly effective approach in studying and characterizing 2D materials. As our understanding of these materials continues to deepen, we can expect to see groundbreaking advancements in various industries, fuelling further innovation and pushing the boundaries of what is possible.
