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 moiré 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 moiré assemblies.

中文翻译：

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人工智能辅助莫尔组件的计算设计

二维 (2D) 层状材料提供了一个材料平台，具有从能源到信息处理设备的潜在应用。尽管已经实现并深入研究了一些单层和少层形式的材料，例如石墨烯和过渡金属二硫属化物，但任意层组件的空间仍然大部分未被探索。这项工作的主要目标是通过仔细选择组成层、它们的堆叠和相对方向来展示对层状材料的电子特性的精确控制。用于自动规划、执行和分析电子结构计算的基于物理和 AI 驱动的方法应用于基于原型一维 (1D) 材料和逼真 2D 材料的分层组件。我们发现可以在 1D 中常规生成具有所需电子特性的莫尔带结构，例如大范围内的任何值的带隙，即使使用很少的层和材料（此处分别为 4 和 6）。我们认为，通过显示基本物理成分在两层 MoS 的计算中已经很明显，这种可调性扩展到 2D 材料₂和多层石墨烯莫尔组件。