论文标题
Moiré集会的计算设计在人工智能的帮助下
Computational Design of Moiré Assemblies Aided by Artificial Intelligence
论文作者
论文摘要
二维(2D)分层材料证明了与散装对应物的特性显着不同的材料,提供了一个材料平台,该材料平台具有从能量到信息处理设备的潜在应用。尽管已经实现和彻底研究了某些单层和几层形式的材料,例如石墨烯和过渡金属二分法,但任意分层的组件的空间仍未得到探索。这项工作的主要目标是通过仔细选择组成层,堆叠和相对取向来证明对分层材料的电子特性的精确控制。基于物理和AI驱动的方法,用于基于原型一维(1D)材料和现实的2D材料,将电子结构计算的自动化计划,执行和分析应用于分层组件。我们发现,有可能在1D中常规生成Moiré带结构具有所需的电子特性,例如在大范围内的任何值的带隙,即使有很少的层和材料(在这里分别为四个和六个)。我们认为,这种可调节性通过显示基本物理成分在两层MOS $ _2 $和多层石墨烯Moiré组件中的计算中已经显而易见。
Two-dimensional (2D) layered materials, demonstrating significantly different properties from their bulk counterparts, offer a materials platform with potential applications from energy to information processing devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrarily layered assemblies is still mostly unexplored. The main goal of this work is to demonstrate precise control of layered materials' electronic properties through careful choice of the constituent layers, their stacking, and relative orientation. Physics-based and AI-driven approaches for the automated planning, execution, and analysis of electronic structure calculations are applied to layered assemblies based on prototype one-dimensional (1D) materials and realistic 2D materials. We find it is possible to routinely generate moiré band structures in 1D with desired electronic characteristics such as a band gap of any value within a large range, even with few layers and materials (here, four and six, respectively). We argue that this tunability extends to 2D materials by showing the essential physical ingredients are already evident in calculations of two-layer MoS$_2$ and multi-layer graphene moiré assemblies.