NSF Convergence Accelerator Track I: Sustainable Topological Energy Materials (STEM) for Energy-efficient Applications
NSF Convergence Accelerator Track I: Sustainable Topological Energy Materials (STEM) for Energy-efficient Applications
批准号:
2235945
负责人:
Mingda Li
金额:
$75.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-12-15 至 2024-02-29
中文摘要
在过去的十年里,一种被称为拓扑量子材料的新材料的发现,代表了凝聚态物理和材料科学的一个重大新前沿。在拓扑材料中,电子的量子态由拓扑来描述和保护,拓扑描述了局部扰动不能改变的稳健的全局性质。拓扑材料在量子信息科学、能量采集和微电子学等领域具有重要的应用价值。然而,尽管实验室演示前景看好,但准备在室温下部署的环境友好型拓扑材料却很少。该项目旨在促进可持续拓扑量子材料的研究,特别强调能效应用。为了实现这些应用,该项目寻求确定有前途的候选材料,评估它们的性能,并设计合适的设备架构。研究小组将通过在拓扑材料理论、材料信息学和机器学习、材料合成、表征和器件制造方面的互补专业知识,系统地寻找、调查和基准具有环境可持续和具有所需拓扑性质的拓扑材料。为了弥合不同研究领域之间以及学术界和工业界之间的现有差距,该项目将开发资源和活动,例如与行业合作伙伴建立数据共享基础设施,并为拓扑材料行业培养未来的劳动力。该团队由来自不同学科(物理、材料工程、电气工程、数据科学)的拓扑材料研究的先驱以及对拓扑材料在微电子和能源应用中的机会感兴趣的行业合作伙伴组成。这项研究将为本科生和研究生创造行业实习机会,鼓励他们追求与行业相关的问题。研究团队将通过虚拟现实增强的互动学习培训不同的拓扑材料行业和数据科学劳动力,并为高中和K-12教师和导师带来资源。这项研究建立在最近发现的拓扑二极管效应的基础上。与传统二极管不同的是,整流需要异质结构或不同掺杂的区域,而拓扑二极管是基于本征Berry曲率偶极子的,它提供了新的光电检测和热电能量收集原理,大大提高了效率。在第一阶段,总体目标包括:(A)一个拓扑材料数据库,其中包括晶体结构、拓扑不变量、合成路径,以及最重要的是拓扑二极管的性能指标。该数据库不仅针对物理学家,还针对固态化学家、材料科学家和半导体行业,以加速大规模生产。(B)确定能够有效地将拓扑材料的结构与功能联系起来的适当描述,并确定环境上最可持续的节能拓扑应用的候选者。这样的描述符,由数据驱动的方法实现,将成为未来拓扑材料发现的基石。(C)为设在麻省理工学院的可持续拓扑能源材料中心奠定基础,该中心将通过会议、论坛和讲习班汇聚学术界和工业界的拓扑材料和能源应用专家。该奖项的目标是让半导体和清洁能源行业与学术界的前沿科学家合作,培育一种拓扑材料解决方案,有助于满足关键的节能技术需求。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The discovery of a new class of materials, known as topological quantum materials, over the past decade represents a major new frontier in condensed matter physics and materials science. In topological materials, the quantum states of electrons are described by, and protected by, topology, which describes robust global properties that local perturbations cannot change. Topological materials are of interest for applications, such as in quantum information science, energy harvesting, and microelectronics. However, despite promising lab demonstrations, environmentally friendly topological materials that are ready for room-temperature deployment are scarce. This project aims at catalyzing research in sustainable topological quantum materials, with a particular emphasis on energy efficient applications. To realize these applications, the project seeks to identify promising material candidates, assess their performance, and design suitable devices architectures. The research team will systematically search for, investigate, and benchmark topological materials that are environmentally sustainable and that have the required topological properties through complementary expertise in topological materials theory, material informatics and machine learning, materials synthesis, characterization, and device fabrication. To bridge existing gaps between different research fields and between academia and industry, the project will develop resources and activities, such as a data-sharing infrastructure with industry partners, and cultivate a future workforce for a topological material industry. The team consists of pioneers in topological materials research from different disciplines (physics, material engineering, electrical engineering, data science) along with industry partners interested in topological materials opportunities for microelectronics and energy applications. This research will create industry internship opportunities for undergraduate and graduate students, encouraging them to pursue industry-relevant problems. The research team will train a diverse workforce of topological material industry and data science through virtual-reality-augmented interactive learning and bring resources to high-school and K-12 teachers and mentors.The research builds on the recent discovery of topological diode effects. Contrary to the conventional diodes, where the rectification requires heterostructures or regions with different doping, a topological diode is based on the intrinsic Berry curvature dipole, which offers new principles in photodetection and thermoelectric energy harvesting with much-improved efficiency. In Phase I, the overarching goals include: (a) A topological materials database, which includes crystal structures, topological invariants, synthesis pathways, and most importantly, performance indicators for topological diodes. The database targets not only physicists, but also solid-state chemists, materials scientists, and semiconductor industries to accelerate large scale production. (b) Identify proper descriptors that can effectively link the structures of topological materials to functionalities, and identify the most environmentally sustainable candidates for energy-efficient topological applications. Such descriptors, enabled by data-driven methods, will serve as the cornerstone for future topological materials discovery. (c) Building the foundation for a Center for Sustainable Topological Energy Materials, based at MIT, that will bring together experts in topological materials and energy applications from academia and industry through meetings, forums, and workshops. The goal is to engage semiconductor and clean-energy industries to collaborate with forefront scientists in academia to foster a topological materials solutions that will contribute to addressing critical needs energy efficient technologies.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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1126/science.adf1506
发表时间:
2023-06
期刊:
Science
影响因子:
56.9
作者:
[Anyuan Gao;Yu-Fei Liu;Jian-Xiang Qiu;B. Ghosh;Thaís V Trevisan;Y. Onishi;Chaowei Hu;Tiema Qian;Hung-Ju Tien;Shaojuan Chen;Mengqi Huang;Damien Bérubé;Houchen Li;C. Tzschaschel;T. Dinh;Zhengyuan Sun;Sheng-Chin Ho;S. Lien;Bahadur Singh;Kenji Watanabe;T. Taniguchi;D. Bell;Hsin Lin;Tay-Rong Chang;C. Du;A. Bansil;L. Fu;Ni Ni-Ni;P. P. Orth-P.;Qiong Ma;Su-Yang Xu]
通讯作者:
Anyuan Gao;Yu-Fei Liu;Jian-Xiang Qiu;B. Ghosh;Thaís V Trevisan;Y. Onishi;Chaowei Hu;Tiema Qian;Hung-Ju Tien;Shaojuan Chen;Mengqi Huang;Damien Bérubé;Houchen Li;C. Tzschaschel;T. Dinh;Zhengyuan Sun;Sheng-Chin Ho;S. Lien;Bahadur Singh;Kenji Watanabe;T. Taniguchi;D. Bell;Hsin Lin;Tay-Rong Chang;C. Du;A. Bansil;L. Fu;Ni Ni-Ni;P. P. Orth-P.;Qiong Ma;Su-Yang Xu
NSF Convergence Accelerator Track I: Advancing Sustainable Topological Material Prototype Devices for Energy-efficient Applications
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批准号:2345084
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项目类别:Cooperative Agreement
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资助金额:$500.0万
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财政年份:2023
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负责人:Mingda Li
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依托单位:
Collaborative Research: DMREF: Symmetry-Guided Machine Learning for the Discovery of Topological Phononic Materials
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批准号:2118448
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项目类别:Standard Grant
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资助金额:$56.0万
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财政年份:2021
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负责人:Mingda Li
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依托单位:
海外基金