CAREER: Quantum defects in two-dimensional materials by local-symmetry-guided data-driven design

职业：通过局域对称引导的数据驱动设计研究二维材料中的量子缺陷

• 批准号: 2314050
• 负责人: Qimin Yan
• 金额: $ 50.8万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314050&HistoricalAwards=false
• 关键词: CAREER; Quantum; defects; two; dimensional

This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2).NONTECHNICAL SUMMARYThis CAREER award supports theoretical and computational research integrated with education activities to advance the fundamental understanding of quantum defects and discover novel functional material systems for quantum information science and technologies. Qubits and novel quantum devices such as quantum emitters lie at the center of the ongoing quantum information revolution that is expected to transform science and society in previously unimaginable ways. Quantum defects, such as missing atoms or impurities, in two-dimensional materials offer a new paradigm for the realization of patterned fabrication and operation of quantum functionality components. By incorporating symmetry-guided design principles and data-driven approaches, the PI and his team will facilitate breakthroughs for the discovery and design of novel quantum defects with unique electronic structures for quantum information science and technologies. The research will pave the path toward the creation of a quantum defect design platform.The research project will be integrated with educational activities through the incorporation of numerical simulations and machine learning modules, as well as outreach activities to K-12 students. Quantum materials, machine learning, and numerical simulation modules will be incorporated in undergraduate and graduate courses. The construction of an interdisciplinary research environment will allow multi-level students to acquire a complete set of skills and grasp a big picture of quantum information science. The PI will participate in scientific outreach by developing activity kits and demos in collaboration with local science museums including the Franklin Institute. Educational modules for middle school and high school students will be developed to introduce quantum physics and artificial intelligence.TECHNICAL SUMMARYThis CAREER award supports theoretical and computational research activities to develop and utilize first-principles computations and data-driven approaches to provide insights into the quantum phenomena in technologically important defect-based two-dimensional (2D) quantum systems. Quantum defects are characterized by local symmetries and complex interactions with their host materials. The research will advance fundamental understanding of quantum defects in 2D materials as spin qubits and quantum emitters by revealing the interplay of local symmetry and host environment. By harnessing symmetry information and adopting state-of-the-art learning architectures, the PI and his team will develop a novel machine learning framework to enable the use of deep learning for defect property predictions. The ultimate outcome of this project will be to provide fundamental understanding and symmetry-based design principles for targeted quantum defect functionalities in 2D materials and beyond. This will open a new data-driven pathway for quantum information science and technologies based on quantum defects in 2D solid-state systems.The research project will be integrated with educational activities through the incorporation of numerical simulations and machine learning modules, as well as outreach activities to K-12 students. Quantum materials, machine learning, and numerical simulation modules will be incorporated in undergraduate and graduate courses. The construction of an interdisciplinary research environment will allow multi-level students to acquire a complete set of skills and grasp a big picture of quantum information science. The PI will participate in scientific outreach by developing activity kits and demos in collaboration with local science museums including the Franklin Institute. Educational modules for middle school and high school students will be developed to introduce quantum physics and artificial intelligence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。非技术摘要该职业奖项支持与教育活动相结合的理论和计算研究，以增进对量子缺陷的基本理解并发现新颖的功能材料系统量子信息科学与技术。量子位和量子发射器等新型量子设备处于正在进行的量子信息革命的中心，预计将以以前难以想象的方式改变科学和社会。二维材料中的量子缺陷，例如缺失的原子或杂质，为实现量子功能组件的图案化制造和操作提供了新的范例。通过结合对称引导的设计原则和数据驱动的方法，PI 和他的团队将促进量子信息科学和技术中具有独特电子结构的新型量子缺陷的发现和设计的突破。该研究将为创建量子缺陷设计平台铺平道路。该研究项目将通过数值模拟和机器学习模块以及针对 K-12 学生的外展活动与教育活动相结合。量子材料、机器学习和数值模拟模块将纳入本科生和研究生课程。跨学科研究环境的构建，将使多层次的学生获得完整的技能，掌握量子信息科学的大局。 PI 将与包括富兰克林研究所在内的当地科学博物馆合作开发活动套件和演示，参与科学推广。将开发针对中学生和高中生的教育模块，以介绍量子物理和人工智能。技术摘要该职业奖支持理论和计算研究活动，以开发和利用第一原理计算和数据驱动方法，以提供对量子现象的见解在技​​术上重要的基于缺陷的二维（2D）量子系统中。量子缺陷的特点是局部对称性和与其主体材料的复杂相互作用。该研究将通过揭示局部对称性和宿主环境的相互作用，促进对二维材料中量子缺陷（如自旋量子位和量子发射器）的基本理解。通过利用对称信息并采用最先进的学习架构，PI 和他的团队将开发一种新颖的机器学习框架，以利用深度学习进行缺陷属性预测。该项目的最终成果将是为二维材料及其他材料中的目标量子缺陷功能提供基本的理解和基于对称性的设计原则。这将为基于二维固态系统中量子缺陷的量子信息科学和技术开辟一条新的数据驱动途径。该研究项目将通过数值模拟和机器学习模块的结合以及推广与教育活动相结合K-12 学生的活动。量子材料、机器学习和数值模拟模块将纳入本科生和研究生课程。跨学科研究环境的构建，将使多层次的学生获得完整的技能，掌握量子信息科学的大局。 PI 将与包括富兰克林研究所在内的当地科学博物馆合作开发活动套件和演示，参与科学推广。将开发针对中学生和高中生的教育模块，以介绍量子物理和人工智能。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

2023 roadmap for materials for quantum technologies

2023 年量子技术材料路线图

• DOI: 10.1088/2633-4356/aca3f2
• 发表时间: 2023-01
• 期刊: Materials for Quantum Technology
• 作者: Becher, Christoph;Gao, Weibo;Kar, Swastik;Marciniak, Christian D.;Monz, Thomas;Bartholomew, John G.;Goldner, Philippe;Loh, Huanqian;Marcellina, Elizabeth;Goh, Kuan Eng Johnson;et al
• 通讯作者: et al