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Computational design of moiré assemblies aided by artificial intelligence

Georgios Tritsaris1*, Stephen Carr2, Gabriel R. Schleder1,3

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

2 Brown Theoretical Physics Center and Department of Physics, Brown University, Providence, Rhode Island 02912, USA

3 Federal University of ABC (UFABC), Santo André, São Paulo 09210-580, Brazil

* Corresponding authors emails: gtritsaris@seas.harvard.edu
DOI10.24435/materialscloud:7e-pc [version v1]

Publication date: Jun 01, 2021

How to cite this record

Georgios Tritsaris, Stephen Carr, Gabriel R. Schleder, Computational design of moiré assemblies aided by artificial intelligence, Materials Cloud Archive 2021.81 (2021), doi: 10.24435/materialscloud:7e-pc.


Two-dimensional (2D) layered materials offer a materials platform with potential applications from energy to information processing devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrarily layered assemblies is still mostly unexplored. The main goal of this work is to demonstrate precise control of layered materials' electronic properties through careful choice of the constituent layers, their stacking, and relative orientation. Physics-based and AI-driven approaches for the automated planning, execution, and analysis of electronic structure calculations are applied to layered assemblies based on prototype one-dimensional (1D) materials and realistic 2D materials. We find it is possible to routinely generate moiré band structures in 1D with desired electronic characteristics such as a band gap of any value within a large range, even with few layers and materials (here, four and six, respectively). We argue that this tunability extends to 2D materials by showing the essential physical ingredients are already evident in calculations of two-layer MoS2 and multi-layer graphene moiré assemblies.

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External references

Journal reference
G. A. Tritsaris, S. Carr, G. R. Schleder, Applied Physics Reviews 8, 031401 (2021) doi:10.1063/5.0044511


two-dimensional materials electronic structure high-throughput calculations artificial neural networks

Version history:

2021.81 (version v1) [This version] Jun 01, 2021 DOI10.24435/materialscloud:7e-pc