Machine-Intelligence-Driven High-Throughput Prediction of 2D Charge Density Wave Phases

Kabiraj, A and Mahapatra, S (2020) Machine-Intelligence-Driven High-Throughput Prediction of 2D Charge Density Wave Phases. In: Journal of Physical Chemistry Letters . pp. 6291-6298.

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Abstract

Charge density wave (CDW) materials are an important subclass of two-dimensional materials exhibiting significant resistivity switching with the application of external energy. However, the scarcity of such materials impedes their practical applications in nanoelectronics. Here we combine a first-principles-based structure-searching technique and unsupervised machine learning to develop a fully automated high-throughput computational framework, which identifies CDW phases from a unit cell with inherited Kohn anomaly. The proposed methodology not only rediscovers the known CDW phases but also predicts a host of easily exfoliable CDW materials (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 Fermi surface nesting, which causes significant semiconducting gap opening in its CDW phase. Our findings could provide useful guidelines for experimentalists. © Copyright © 2020 American Chemical Society.

Item Type:	Journal Article
Publication:	Journal of Physical Chemistry Letters
Publisher:	American Chemical Society
Additional Information:	The copyright of this article belongs to American Chemical Society
Keywords:	Artificial intelligence; Charge density; Electronic structure; Selenium compounds; Titanium compounds; Zirconium compounds, Associated electronics; Charge-density-wave phasis; Comprehensive analysis; Computational framework; Fermi surface nesting; Searching techniques; Two-dimensional materials; Unsupervised machine learning, Charge density waves
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	17 Aug 2020 06:15
Last Modified:	17 Aug 2020 06:15
URI:	http://eprints.iisc.ac.in/id/eprint/66332

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