Kabiraj Arnab, Mahapatra Santanu
Nano-Scale Device Research Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science (IISc) Bangalore, Bangalore - 560012, India.
J Phys Chem Lett. 2020 Aug 6;11(15):6291-6298. doi: 10.1021/acs.jpclett.0c01846. Epub 2020 Jul 22.
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 ZrTiSe 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.
电荷密度波（CDW）材料是二维材料的一个重要子类，在施加外部能量时会表现出显著的电阻率切换。然而，这类材料的稀缺阻碍了它们在纳米电子学中的实际应用。在这里，我们结合基于第一性原理的结构搜索技术和无监督机器学习，开发了一个全自动的高通量计算框架，该框架从具有继承的科恩反常的晶胞中识别出CDW相。所提出的方法不仅重新发现了已知的CDW相，还预测了大量易于剥离的CDW材料（30种材料和114个相）以及相关的电子结构。在众多有前景的候选材料中，我们特别关注ZrTiSe，并进行了全面分析以深入了解费米面嵌套，这在其CDW相中导致了显著的半导体能隙打开。我们的发现可为实验人员提供有用的指导。
