Using machine intelligence to find the charge density wave phases of any 2D material

(Nanowerk Spotlight) Charge density wave (CDW) is a quantum mechanical phenomenon, which induces distortion in the crystal structures of some low-dimensional (1D or 2D) metals, when the temperature is reduced.
Such distorted crystal structure is known as CDW phase and its resistivity is much higher than the original symmetric phase. Since the switching between symmetric and CDW phase can also be made by the application of external electric field, these materials are technologically important and have attracted immense attention in the nanoelectronics community.
They find applications in memristive-based in-memory computing, transistor-less logic circuit and oscillatory neural networks.
The rapid computational screening for exotic and application-specific properties in 2D materials (e.g. non-trivial topological order, high-temperature ferromagnetism, superior catalytic activity etc.) is becoming increasingly important.
First-principle-based rapid prediction of CDW phases in 2D materials has not yet been reported. Currently, there is a gaping scarcity of suitable 2D CDW materials for nanoelectronic device applications.
We bridge this gap by developing an automated high-throughput computational tool combining first-principles-based structure-searching technique and unsupervised machine learning, which identifies CDW phases from a unit cell with inherited Kohn anomaly.
Our findings have been reported in The Journal of Physical Chemistry Letters ("Machine-Intelligence-Driven High-Throughput Prediction of 2D Charge Density Wave Phases")
Automated workflow to find CDW phases of a material
Automated workflow to find charge density wave phases of a material. A specific naming convention has been used throughout this Letter to identify the normal and CDW phases properly. The normal phase is called Φ0,; the CDW phase just less stable than Φ0 is called Φ-1; the phase just less stable than Φ-1 is named Φ-2, and so on. (Reprinted with permission by American Chemical Society) (click on image to enlarge)
Although machine learning techniques are now being extensively used in material sciences, these studies mostly involve supervised regression to predict certain material properties.
Here we deploy unsupervised classification, thanks to which, we find a host of undiscovered phases for even extensively studied 2D CDW materials such as 1T-TaS2 and 2H-NbSeS2.
The proposed methodology not only rediscovers the known CDW phases but also predicts a host of easily exfoliable new CDW materials (a total 30 materials and 114 phases) along with associated electronic structures.
Among many promising candidates, we pay special attention to ZrTiSe4 and conduct a comprehensive analysis to gain insight into the quantum-mechanical-phenomana called ‘Fermi-surface-nesting’, which causes significant semiconducting gap opening in its CDW phase.
Watch a discussion by the authors of this work on machine-intelligence-driven exploration of 2D charge density waves.
Our findings could provide useful guidelines for the experimentalists and foster practical application of CDW materials in nanoelectronics.
Provided by Nano Scale Device Research Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science Bangalore as a Nanowerk exclusive
 

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