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Collaborative Research: DMREF: Symmetry-Guided Machine Learning for the Discovery of Topological Phononic Materials

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
负责人:
Mingda Li
金额:
$56.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-10-01 至 2025-09-30

项目摘要

项目成果

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中文摘要
翻译
非技术描述:基本了解和控制材料中的热传导过程对于能源基础设施、电子设备和可再生能源发电系统非常重要。这个项目专注于声子的一种新特性--材料中携带热量的原子的振动--称为“拓扑学”。这一性质可能会产生新的现象,如垂直于温度梯度方向的热传导和更有效的热波在材料表面的传输。为了发现拓扑声子,研究小组将利用材料基因组方法来搜索拥有这些特殊热载体的材料。一旦确定了候选人,研究团队将对他们进行合成和表征,结果将被用来改进搜索算法。研究小组计划建立一个公共数据库,存储大量材料的导热属性。这项研究不仅将促进对真实材料中拓扑结构如何影响热传导的基本理解,还将为实现热导体等可开关的不寻常功能提供新的途径。该项目还支持通过实践课程项目和短期课程向K-12和本科生教授基本材料物理概念的教育活动。为了促进材料科学劳动力的多样性,该团队还为来自代表性不足的少数族裔社区的高中生和本科生提供研究机会。技术描述:虽然在过去的十年里,电子态的拓扑一直是凝聚态物理学的中心主题,但拓扑声子态却没有受到太多的关注。与费米子不同,整个声子谱中的拓扑态可以对可观察到的材料性质做出贡献,使拓扑声子材料成为拓扑玻色子系统中新兴物理的理想试验台,包括声子热霍尔效应、新颖的拓扑声-电子相互作用以及由此产生的现象,如异常超导态。该项目旨在系统地识别在热区中含有本征拓扑声子的材料,其中拓扑声子态明确地改变本征材料的性质,包括热输运、电子-声子相互作用和表面声子模。研究小组将寻求通过融合基于欧几里得神经网络的对称性引导的机器学习来加速材料发现。机器学习预测将通过第一原理声子模拟和拓扑不变性分析得到验证。有希望的候选材料将被合成成薄膜和大块单晶,并用非弹性中子和X射线散射、热输运、表面敏感光谱和扫描探针测量来表征。这项研究将促进对拓扑玻色子系统的基本理解,并检查由拓扑声子实现的新的热功能。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
Non-technical Description: Fundamental understanding and control of heat conduction processes in materials are important for energy infrastructure, electronic devices, and renewable energy generation systems. This project focuses on a novel property of phonons – vibrations of atoms that carry the heat in materials - called "topology". This property may allow new phenomena, such as heat conduction perpendicular to the temperature gradient direction and more efficient transport of heat waves on the material surfaces. To discover topological phonons, the research team will exploit a Materials Genome approach to search for materials hosting these special heat carriers. Once candidates are identified, the research team will synthesize and characterize them, and the results will be used to refine the search algorithm. The research team plans to establish a public database storing the heat conduction properties of a large number of materials. This research will not only advance the fundamental understanding of how topology affects heat conduction in real materials, but also provide new routes to realizing unusual functionalities such as heat conductors that can be switched on and off. This project also supports educational activities to teach basic materials physics concepts to K-12 and undergraduate students through hands-on class projects and short courses. To promote diversity in the materials science workforce, the team also provides research opportunities to high school and undergraduate students from underrepresented minority communities. Technical Description: While the topology of electronic states has been a central theme in condensed matter physics for the past decade, topological phononic states have received much less attention. Unlike their fermionic counterparts, topological states in the entire phonon spectrum can contribute to observable material properties, making topological phononic materials ideal testbeds for emerging new physics in topological bosonic systems, including phonon thermal Hall effects, novel topological phonon-electron interactions and the resulting phenomena, such as unusual superconducting states. This project aims to systematically identify materials hosting intrinsic topological phonons in the thermal regime, where the topological phononic states explicitly modify intrinsic material properties, including thermal transport, electron-phonon interactions, and surface phonon modes. The research team will seek to accelerate material discovery by incorporating symmetry-guided machine learning based on Euclidean neural networks. Machine learning predictions will be verified using first-principles phonon simulation and topological invariance analysis. Promising candidate materials will be synthesized as thin films and bulk single crystals and characterized using inelastic neutron and x-ray scattering, thermal transport, and surface-sensitive spectroscopy and scanning probe measurements. This research will advance fundamental understanding of topological bosonic systems and examine new thermal functionalities enabled by topological phonons.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(2)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1002/adma.202206997
发表时间: 2022
期刊: Advanced Materials
影响因子: 29.4
作者: [Chen, Zhantao, Shen, Xiaozhe, Andrejevic, Nina, Liu, Tongtong, Luo, Duan, Nguyen, Thanh, Drucker, Nathan C., Kozina, Michael E., Song, Qichen, Hua, Chengyun]
通讯作者: Hua, Chengyun
NSF Convergence Accelerator Track I: Advancing Sustainable Topological Material Prototype Devices for Energy-efficient Applications
NSF Convergence Accelerator Track I: Sustainable Topological Energy Materials (STEM) for Energy-efficient Applications
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
Cell Research
Cell Research (细胞研究)