NSF Convergence Accelerator Track I: Sustainable Topological Energy Materials (STEM) for Energy-efficient Applications

NSF 融合加速器轨道 I:用于节能应用的可持续拓扑能源材料 (STEM)

基本信息

  • 批准号:
    2235945
  • 负责人:
  • 金额:
    $ 75万
  • 依托单位:
  • 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
  • 起止时间:
    2022-12-15 至 2024-02-29
  • 项目状态:
    已结题

项目摘要

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.
在过去的十年中,被称为拓扑量子材料的一类新材料的发现代表了凝聚态物理和材料科学的一个重要的新前沿。在拓扑材料中,电子的量子态由拓扑描述并受拓扑保护,拓扑描述了局部扰动无法改变的鲁棒全局特性。拓扑材料在量子信息科学、能量收集和微电子学等领域具有广泛的应用前景。然而,尽管有很好的实验室演示,可用于室温部署的环境友好型拓扑材料仍然很少。该项目旨在促进可持续拓扑量子材料的研究,特别强调节能应用。为了实现这些应用,该项目寻求确定有前途的候选材料,评估其性能,并设计合适的器件架构。研究团队将通过在拓扑材料理论、材料信息学和机器学习、材料合成、表征和设备制造方面的互补专业知识,系统地搜索、调查和测试具有环境可持续性并具有所需拓扑特性的拓扑材料。为了弥合不同研究领域之间以及学术界与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(物理学,材料工程,电气工程,数据科学)的拓扑材料研究先驱以及对微电子和能源应用拓扑材料机会感兴趣的行业合作伙伴组成。本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互动学习,培养拓扑材料行业和数据科学的多元化劳动力,并为高中和K-12教师和导师提供资源。这项研究建立在最近发现的拓扑二极管效应的基础上。与传统二极管需要异质结构或不同掺杂区域的整流不同,拓扑二极管基于本征贝里曲率偶极子,为光电探测和热电能量收集提供了新的原理,效率大大提高。在第一阶段,总体目标包括:(a)拓扑材料数据库,其中包括晶体结构,拓扑不变量,合成途径,最重要的是,拓扑二极管的性能指标。该数据库的目标不仅是物理学家,还包括固态化学家、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,并确定最具环境可持续性的节能拓扑应用候选材料。这样的描述符,通过数据驱动的方法,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建立基础,该中心将通过会议、论坛和讲习班汇集学术界和工业界拓扑材料和能源应用方面的专家。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,促进拓扑材料解决方案,这将有助于解决节能技术的关键需求。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识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

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Quantum metric nonlinear Hall effect in a topological antiferromagnetic heterostructure
  • DOI:
    10.1126/science.adf1506
  • 发表时间:
    2023-06
  • 期刊:
  • 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
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Mingda Li其他文献

Coarse-Grained Reduced MoxTi1−xNb2O7+y Anodes for High-Rate Lithium-ion Batteries
高倍率锂离子电池粗晶还原MoxTi1-xNb2O7 y阳极
  • DOI:
    10.1016/j.ensm.2020.10.016
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    20.4
  • 作者:
    Lijiang Zhao;Shitong Wang;Yanhao Dong;Wei Quan;Fei Han;Yimeng Huang;Yutong Li;Xinghua Liu;Mingda Li;Zhongtai Zhang;Junying Zhang;Zilong Tang;Ju Li
  • 通讯作者:
    Ju Li
A method for assessing the risk of rockburst based on coal-rock mechanical properties and In-Situ ground stress
一种基于煤岩力学性质和原地地应力的岩爆风险评估方法
  • DOI:
    10.1038/s41598-024-76971-0
  • 发表时间:
    2024-10-30
  • 期刊:
  • 影响因子:
    3.900
  • 作者:
    Hai Rong;Nannan Li;Chen Cao;Yadi Wang;Shilong Wei;Jincheng Li;Mingda Li
  • 通讯作者:
    Mingda Li
Image tampering detection based on RDS-YOLOv5 feature enhancement transformation
  • DOI:
    10.1038/s41598-024-76388-9
  • 发表时间:
    2024-10-30
  • 期刊:
  • 影响因子:
    3.900
  • 作者:
    Meilong Zhu;Mingda Li;Zhaohui Wang
  • 通讯作者:
    Zhaohui Wang
Combustion kinetics and ash particles structure analysis of biomass in-situ and cooling char
生物质原位及冷却炭的燃烧动力学和灰颗粒结构分析
  • DOI:
    10.1016/j.energy.2025.134883
  • 发表时间:
    2025-03-01
  • 期刊:
  • 影响因子:
    9.400
  • 作者:
    Mingda Li;Guangqian Luo;Renjie Zou;Wencong Qiu;Yi Xiao;Guangwen Xu;Hong Yao
  • 通讯作者:
    Hong Yao
Clustering Algorithm of Similarity Segmentation based on Point Sorting
基于点排序的相似度分割聚类算法
  • DOI:
    10.2991/lemcs-15.2015.91
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    5.8
  • 作者:
    Han;Yan Wang;Lan Huang;Mingda Li;Ying Sun;Hanyuan Zhang
  • 通讯作者:
    Hanyuan Zhang

Mingda Li的其他文献

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{{ truncateString('Mingda Li', 18)}}的其他基金

NSF Convergence Accelerator Track I: Advancing Sustainable Topological Material Prototype Devices for Energy-efficient Applications
NSF 融合加速器轨道 I:推进可持续拓扑材料原型器件的节能应用
  • 批准号:
    2345084
  • 财政年份:
    2023
  • 资助金额:
    $ 75万
  • 项目类别:
    Cooperative Agreement
Collaborative Research: DMREF: Symmetry-Guided Machine Learning for the Discovery of Topological Phononic Materials
合作研究:DMREF:用于发现拓扑声子材料的对称引导机器学习
  • 批准号:
    2118448
  • 财政年份:
    2021
  • 资助金额:
    $ 75万
  • 项目类别:
    Standard Grant

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