Machine-Intelligence-Driven High-Throughput Prediction of 2D Charge Density Wave Phases
Arnab Kabiraj and Santanu Mahapatra*
J. Phys. Chem. Lett. 2020, 11, 15, 6291–6298
Publication Date:July 22, 2020
DOI: 10.1021/acs.jpclett.0c01846
*Nano-Scale Device Research Laboratory, IISc Bangalore, India
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.
Fig: Top view of TaSe2-H 3×3ΙΈ-1.
