Computational design of moire assemblies aided by artificial intelligence

Author(s)

Tritsaris, Georgios A. ^[1] ; Carr, Stephen ^{[2, 3]} ; Schleder, Gabriel R. ^{[4, 1]}

Affiliation

^[1] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 - USA

^[2] Brown Univ, Dept Phys, Providence, RI 02912 - USA

^[3] Brown Univ, Brown Theoret Phys Ctr, Providence, RI 02912 - USA

^[4] Fed Univ ABC UFABC, BR-09210580 Santo Andre, SP - Brazil

• Document type: Journal article
• Source: APPLIED PHYSICS REVIEWS; v. 8, n. 3 SEP 2021.
• Web of Science Citations: 0

Abstract

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 arbitrary 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 moire band structures in 1D with desired electronic characteristics such as a bandgap 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 moire assemblies. (AU)

• FAPESP's process: 17/18139-6 - Machine learning for Materials Science: 2D materials discovery and design
• Grantee: Gabriel Ravanhani Schleder
• Support Opportunities: Scholarships in Brazil - Doctorate