In the last five years, the discovery and significant interest in twisted bilayer graphene has given rise to a completely new subfield in advanced materials science and quantum physics: twistronics. In a new study, scientists apply machine learning algorithms to find out more about this extraordinary phenomenon.
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Discovering the magic angle: twisted bilayer graphene
The discovery of two-dimensional graphene shook the scientific world when it was published a few decades ago. Since then, graphene innovation has advanced significantly in terms of synthesis methods, understanding of its unique properties, and development of practical applications.
In 2018, researchers working at the Massachusetts Institute of Technology (MIT) in the United States discovered that they could make graphene superconducting by stacking two thick layers of material atoms on top of each other at a precise angle. 1.1 °.
In this orientation, which researchers called the “magic angle of rotation,” bilayer graphene is transformed from a weakly correlated Fermi liquid into a strongly correlated two-dimensional electron system. The properties of the system are exceptionally sensitive to carrier density, proximity to nearby doors, and variation in angle of rotation.
Twisted bilayer graphene was found to have several unique properties. It is superconducting, but also has interaction-induced insulating states, electron nematicity, low-temperature linear resistivity, quantized anomalous Hall states, and magnetism.
Twistronics Challenges
The discovery of twisted graphene gave rise to a new subfield in materials science and quantum physics: twistronics.
Scientists working in this field are now looking for more two-dimensional materials and stacking angles that can produce remarkable properties.
Researchers are focusing on finding magic angles for other van der Waals materials (such as graphene) that have a fundamental magnetic state, either antiferromagnetic or ferromagnetic, when produced in ultrafine layers.
But discovering magic angles like the 1.1 ° angle of graphene is no easy task.
High-resolution microscopy using techniques such as Transmission Electron Microscopy (TEM) or Scanning Probe Microscopy (SPM) can help researchers accurately measure rotation angles with an accuracy of less than 0.01 °. These techniques, however, are time consuming and require samples to be independent or supported on a conductive substrate.
Not only that, but these measurements can also only provide very local information on areas smaller than one micrometer: angles of rotation can vary considerably by just a few micrometers.
Practically, then, this approach is not viable. Practical applications of twisted bilayer materials require characterizations on arbitrary substrates over a much larger area and in a relatively shorter period of time.
Other techniques include measures to transport devices under magnetic fields and at low temperatures, but this approach is complex and limited to small areas.
Low-energy electron diffraction (LEED) provides information about the stacked layers and their orientations, covering areas larger than TEM or SPM. However, the method also requires a conductive substrate and high vacuum conditions.
Raman spectroscopy is a promising technique for the massive identification of magic angles because it obtains a large amount of information about the state of the material of its Raman spectrum at the same time. However, Raman spectrum differences are often extremely subtle, so using the technique to manually identify angles of rotation is very long and tedious.
Use machine learning to advance graphene innovation
Fast, accurate and non-destructive methods are needed to discover angles of rotation to continue innovation in this field.
In a new study, scientists at Kyushu University in Japan propose a machine learning analysis technique to automatically classify the Raman spectrum of twisted graphene samples into a series of rotation angles.
The study was published in the journal Applied Nano Materials in 2022 along with an open source database for the machine learning algorithm developed by the researchers.
This algorithm has low computational demands; it is fast and has around 99% accuracy compared to manual spectrum labeling.
The described method benefits from the flexible and non-invasive nature of Raman spectroscopy measurements combined with the speed and predictive accuracy of machine learning. As a result, the authors argued that it could facilitate exploration in the emerging field of twistronics.
The method involves extracting features of the Raman spectrum from the twisted graphene, which were used to train the machine learning model to infer the angle of rotation within predefined intervals.
The flexibility of the Raman spectroscopy technique means that the method can also be extended to determine how much voltage and doping there is in graphene samples.
The method could also be applied, according to the researchers, to almost any other heterostructure. This means that the field of twistronics can be used to find more magical materials and angles that may well see significant practical applications in the coming years.
Not only that, but research has also shown how open source machine learning algorithms could enable easy and effective integration of artificial intelligence into other techniques and fields of research.
Continue reading: What is twisted graphene?
References and additional reading
Andrei, EY and AH MacDonald (2020). Graphene bilayer with a twist. Nature. doi.org/10.1038/s41563-020-00840-0.
Cao, Y., V. Fatemi, S. Fang, et al. (2018). Unconventional superconductivity in magic angle graphene supergels. Nature. doi.org/10.1038/nature26160.
Park, JG (2016). Opportunities and challenges of van der Waals 2D magnetic materials: magnetic graphene? Physics journal: condensed matter. doi.org/10.1088/0953-8984/28/30/301001.
Pilkington, B. (2022). What is twisted graphene? [Online] AZO Nano. Available at:
Solís-Fernández, P., and H. Ago (2022). Determination of automatic learning of the angle of rotation of bilayer graphene by Raman spectroscopy: implications for van der Waals heterostructures. Applied nanomaterials. doi.org/10.1021/acsanm.1c03928.
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