A faster way to study 2D materials for next-gen electronics
Two-dimensional materials, consisting of a single layer of atoms, exhibit unusual properties that could be exploited for a wide range of quantum and microelectronic systems. But what really makes them special are their flaws. “Therein lies their real magic,” said Alexander Weber-Bargioni of the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab). Defects down to the atomic level can influence the material’s macroscopic function and lead to new quantum behaviors, and there are so many types of defects that researchers are only just beginning to understand the possibilities. One of the biggest challenges in the field is to systematically study these defects at relevant scales, or with atomic resolution.
Artificial intelligence suggests a way forward. Berkeley Lab researchers recently unveiled a new method that is fast and easily reproducible map and identify defects in two-dimensional materials. It uses convolutional neural networks, which are an application of artificial intelligence, to rapidly analyze data from autonomous experiments, which in recent years have become a powerful tool for imaging these exotic materials.
“Flaws can be used to advantage, or they can cause problems with the macroscopic function of the material,” said John Thomas, postdoctoral researcher with the Weber-Bargioni group at the Molecular Foundry, a user facility at the DOE Office of Science at Berkeley Lab. where this research was conducted. Thomas designed the approach that combines AI with autonomous discovery. “This combination gives us a good way to track down flaws and measure them,” he said. The method could significantly reduce the time needed to characterize two-dimensional materials and use them in next-generation quantum and electronic devices. The scientists reported their research in an article published in npj Computer materials.
Understanding the quantum properties of solids has enabled technological advances over the decades, such as the transistor and the laser. Now, as scientists pursue other applications that harness quantum information science, such as quantum sensing and computing, it is essential to better understand a phenomenon in solids called quantum coherence. This is the goal of the Center for Novel Pathways to Quantum Coherence in Materials (NPQC), an Energy Frontier Research Center (EFRC) led by Berkeley Lab. The center’s goal is to significantly improve scientists’ understanding and control of coherence in solids, which could lead to new devices and applications. And a big part of that work is studying the smallest flaws in a material.
In this specific research, which was supported by the NPQC EFRC, Thomas and Weber-Bargioni, co-PIs of the EFRC, collaborated with Marcus Noack of the Berkeley Lab’s Applied Mathematics and Computational Research Division. Noack, head of autonomous and autonomous experiments at Berkeley Lab’s Center for Advanced Mathematics for Energy Research Applications (CAMERA), developed gpCAM, the system used for autonomous experiments. The group tested their AI-enhanced approach on a material consisting of a single layer of tungsten disulfide (WS2) grown on a substrate of graphene and silicon carbide.
Collecting high-resolution spectroscopic data on sulfur vacancies (a kind of defect) in a square sample of the material measuring 125×125 pixels would take about 23 days using the conventional scanning tunneling microscopy (STM) approach. STM offers a powerful way to collect spectroscopic surface information and connect it to macroscopic phenomena, but creating a full spectral picture, Thomas said, can often be complicated by a number of factors that can arise during a such a long time.
Reducing the time needed to acquire the data could reduce the risk of these complications. By combining STM measurements with machine learning tools, the new approach reduced imaging time to around eight hours.
“About three weeks to a third of a day,” Thomas said. “It’s a good leap forward.”
WS2 is a transition metal dichalcogenide (TMD), a material whose properties make it attractive for applications such as quantum emitters, devices capable of producing a single photon at a time and which could lead to other quantum applications . Additionally, defects such as sulfur vacancies in TMDs suggest exotic new ways of manipulating electrons and photons in electronic devices.
But WS2 is just the beginning. The new technique could be used to generate high-dimensional surface data on nearly any type of two-dimensional material, Thomas said, and lead to the kind of high-resolution, systematic study the field needs. Additionally, the method can be extended beyond STM to other spectroscopic techniques, including atomic force spectroscopy, photo STM, and ultrafast STM. It is available for public use as an open access software package called gpSTS, of which Thomas is the lead developer.
“Hopefully we’ve created a tool that anyone can use and add to most STMs,” Thomas said. “For my part, we will continue to explore different quantum materials and new defects.”
The machine learning component of this research benefited from the expertise of CAMERA, which aims to provide the new fundamental mathematics needed to capitalize on experimental research in science facilities.
– This press release was originally published on the Lawrence Berkeley National Laboratory website