materialscloud:2020.0050/v1

Electronic structure calculations of twisted multi-layer graphene superlattices

Georgios A. Tritsaris1*, Stephen Carr2, Ziyan Zhu2, Yiqi Xie1, Steven B. Torrisi2, Jing Tang3, Marios Mattheakis1, Daniel Larson2, Efthimios Kaxiras2*

1 John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA

2 Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA

3 Department of Physics, Nanjing University, Nanjing, 210093, China

* Corresponding authors emails: gtritsaris@seas.harvard.edu , kaxiras@physics.harvard.edu
DOI10.24435/materialscloud:2020.0050/v1 [version v1]

Publication date: May 04, 2020, 00:00:00

How to cite this record

Georgios A. Tritsaris, Stephen Carr, Ziyan Zhu, Yiqi Xie, Steven B. Torrisi, Jing Tang, Marios Mattheakis, Daniel Larson, Efthimios Kaxiras, Electronic structure calculations of twisted multi-layer graphene superlattices, Materials Cloud Archive 2020.0050/v1 (2020), doi: 10.24435/materialscloud:2020.0050/v1.

Description

Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

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File name Size Description
README.txt
MD5md5:029858f6c241a93647a7068f59beb6db
1002 Bytes Short description of content.
BandStructures.zip
MD5md5:86495c20525156c99e8761027fcb6cc6
393.8 MiB Tight binding band structures of twisted multi-layer graphene superlattices.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

External references

Preprint
Journal reference
G. A. Tritsaris, S. Carr, Z. Zhu, Y. Xie, S. B. Torrisi, J. Tang, M. Mattheakis, D. Larson, E. Kaxiras, 2D Materials (2020) doi:10.1088/2053-1583/ab8f62
Preprint (Paper in which a materials notation for 2D layered assemblies is described.)

Keywords

Graphene High-throughput calculations Machine learning Quantum devices Tight binding band structures

Version history:

2020.0050/v1 (version v1) [This version] May 04, 2020, 00:00:00 DOI10.24435/materialscloud:2020.0050/v1