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: 2118523
• 负责人: Bolin Liao
• 金额: $ 104万
• 依托单位: University of California-Santa Barbara
• 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
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118523&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Symmetry; Guided

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.

非技术描述：材料中热传导过程的基本理解和控制对于能源基础设施，电子设备和可再生能源产生系统很重要。该项目着重于声子的新型属性 - 含有材料中热量的原子的振动 - 称为“拓扑”。该特性可能允许新现象，例如垂直于温度梯度方向的热传导以及在材料表面上更有效地传输热浪。为了发现拓扑声子，研究团队将利用一种材料基因组方法来搜索托管这些特殊热载体的材料。一旦确定了候选人，研究团队将合成并对其进行表征，结果将用于完善搜索算法。研究小组计划建立一个公共数据库，以存储大量材料的热传导性能。这项研究不仅将进一步了解拓扑如何影响真实材料中的热传导，而且还为实现不寻常功能（例如可以打开和关闭的热导体）提供了新的途径。该项目还支持教育活动，通过动手班项目和短课程向K-12和本科生教授基本材料物理概念。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民族社区的高中和本科生提供了研究机会。技术描述：虽然电子状态的拓扑一直是过去十年中凝聚态物理学的中心主题，但拓扑声音状态的关注程度要少得多。与他们的费米子对应物不同，整个声音光谱中的拓扑状态可以促进可观察的材料属性，使拓扑语音材料理想的拓扑性波音系统中新型物理学的理想测试床，包括光子热霍尔效果，新型拓扑式语音效果，新型拓扑语音 - 电子 - 电子相互作用以及结果现象，例如不匹配的超级理解状态。该项目的目的是系统地识别托管在热态度中内在拓扑声子的材料，拓扑声子明确修改了固有的材料特性，包括热传输，电子 - 音纸相互作用和表面声子模式。研究团队将通过根据欧几里得神经元网络合并对称性引导的机器学习来试图加速材料发现。机器学习预测将使用第一原理声子模拟和拓扑不变性分析进行验证。有前途的候选材料将被合成为薄膜和大块的单晶，并使用非弹性中子和X射线散射，热传输以及表面敏感的光谱和扫描探针测量来表征。这项研究将促进对拓扑骨气系统的基本理解，并检查由拓扑声子启用的新热功能。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响来评估，被认为是珍贵的支持。