ECCS-CDS&E: Predictive modeling of atomically thin multifunctional semiconductors

ECCS-CDS

• 批准号: 1607796
• 负责人: Emmanouil Kioupakis
• 金额: $ 34.23万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1607796&HistoricalAwards=false
• 关键词: ECCS; CDS& Predictive; modeling; atomically

Atomically thin two-dimensional materials are promising for novel electronic and optoelectronic device applications. Most work on two-dimensional materials focuses on graphene and transition metal dichalcogenides. This work focuses on materials that contain elements with lone-pair electrons such as tin selenide, which crystallize in layered structures and simultaneously display many interesting functional properties. The project will use state-of-the-art predictive materials simulation algorithms to uncover the fundamental materials physics and functional properties of these novel two-dimensional materials and identify promising candidates for electronic and optoelectronic device applications. The proposed work also involves the development and application of innovative simulation algorithms and high-performance computing tools for the high-fidelity predictive modeling of novel materials and devices in order to advance the current frontiers in electronics. This project will also advance the education, training, and mentoring of graduate and undergraduate students in materials physics, electronic and optoelectronic devices, computational techniques, and high-performance computing. The proposed work also will produce open-source computer codes that will be shared with the educational and research communities. The research findings will be incorporated in the undergraduate curriculum and communicated to the general public through outreach presentations.The goal of the proposed research is to understand and predict the structural and functional properties (structure, thermodynamics, band structure, vibrational properties, electronic and thermal transport, optical response, strain effects, band alignments) of freestanding and substrate-deposited atomically thin layered semiconducting materials with predictive calculations based on density functional theory and related methods. Materials to explore include SnSe and GeSe, which contain cations with lone-pair electrons. These materials crystallize in layered structures and display an array of unique functional properties, such as anisotropic spin transport and unusually strong optical absorbance in the visible range. A genetic algorithm will be applied to identify promising new materials that simultaneously exhibit multiple functionalities for novel electronic, spintronic, and optoelectronic device applications. The findings of the research project are expected to advance the development and manufacturing of novel electronic and optoelectronic devices with functionalities that are limited by current materials availability.

原子级薄的二维材料在新型电子和光电子器件中具有广阔的应用前景。大多数二维材料的工作都集中在石墨烯和过渡金属二硫属元素上。这项工作的重点是材料，包含元素与孤对电子，如硒化锡，结晶在层状结构，同时显示许多有趣的功能特性。 该项目将使用最先进的预测材料模拟算法来揭示这些新型二维材料的基本材料物理和功能特性，并确定电子和光电器件应用的有前途的候选者。拟议的工作还涉及创新模拟算法和高性能计算工具的开发和应用，用于对新型材料和器件进行高保真预测建模，以推进当前的电子前沿。该项目还将推进材料物理，电子和光电器件，计算技术和高性能计算方面的研究生和本科生的教育，培训和指导。拟议的工作还将产生开放源代码的计算机代码，将与教育和研究界共享。研究结果将纳入本科课程，并通过外展演示向公众传达。拟议研究的目标是了解和预测结构和功能特性（结构，热力学，能带结构，振动性质，电子和热输运，光学响应，应变效应，带排列）的独立和衬底沉积的原子薄层状半导体材料的预测计算的基础上密度泛函理论和相关方法。要探索的材料包括SnSe和GeSe，它们含有带有孤对电子的阳离子。这些材料以层状结构结晶，并显示出一系列独特的功能特性，如各向异性自旋输运和在可见光范围内异常强的吸光度。遗传算法将被应用于确定有前途的新材料，同时表现出多种功能的新型电子，自旋电子和光电器件的应用。该研究项目的结果有望推动新型电子和光电器件的开发和制造，这些器件的功能受到当前材料可用性的限制。