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：对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发电系统非常重要。该项目重点关注声子的一个新特性——在材料中传递热量的原子振动。这种特性可能会产生新的现象，例如垂直于温度梯度方向的热传导和材料表面上更有效的热波传输，研究小组将利用材料基因组方法来发现。一旦确定了候选材料，研究团队将对它们进行合成和表征，结果将用于完善搜索算法。研究团队计划建立一个存储热传导特性的公共数据库。这项研究不仅将促进对拓扑如何影响实际材料中的热传导的基本理解，而且还为实现诸如可以打开和关闭的热导体等异常功能提供了新的途径。教授基本材料物理概念的教育活动通过实践课程项目和短期课程，K-12 学生和本科生能够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群体的高中生和本科生提供研究机会。过去十年来，拓扑态一直是凝聚态物理学的中心主题，但与费米子单位不同，拓扑声子态受到的关注要少得多，整个声子谱中的拓扑态可以对可观察到的材料特性做出贡献，从而使拓扑声子态成为可能。材料是拓扑玻色子系统中新兴物理的理想测试平台，包括声子热霍尔效应、新颖的拓扑声子-电子相互作用以及由此产生的现象，例如不寻常的超导态。该项目旨在系统地识别热状态下具有固有拓扑声子的材料。 ，其中拓扑声子态明确地改变了固有材料特性，包括热传输、电子-声子相互作用和表面声子模式。研究团队将寻求通过结合基于对称引导的机器学习来加速材料发现。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块状单晶，并使用非弹性中子和 X 射线散射、热传输和表征。表面敏感光谱和扫描探针测量将促进对拓扑玻色子系统的基本理解，并研究拓扑声子实现的新热功能。该奖项是 NSF 的法定使命。通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。