EAGER:SUPER: Collaborative Research: Ab Initio Engineering of Doped-Covalent-Bond Superconductors

这项EAGER奖支持一项联合计算和理论工作，以指导对实用超导材料的研究。超导体在冷却到某一与材料相关的临界温度以下时，可以无电阻地传输电流。从磁悬浮列车到大型强子对撞机，这一非凡的特性已经得到了许多应用，但目前的超导体很难制造，或者需要超低温才能发挥作用。新型超导材料可以大规模生产，并在易于维持的温度下操作，有可能彻底改变能源、交通、通信和其他新兴技术。设计新的超导体是出了名的困难，因为它们的性质对化学成分和晶体结构很敏感。在这个项目中，该团队将专注于探索包括硼、碳和各种金属在内的轻元素的有前途的组合。pi将采用先进的建模方法和计算工具，在他们的小组开发，以确定和分析合适的候选材料。稳定化合物的寻找将结合进化算法和机器学习原子间势进行。可行的化合物将通过基于万尼尔函数的计算方法进行检测，万尼尔函数是预测超导特性的一种最先进的方法。pi将通过为小学生组织外展活动，并让来自代表性不足群体的学生参与STEM研究，为下一代科学家的发展做出贡献。所有添加到团队开源包中的新计算特性都将公开提供。这项EAGER奖支持旨在识别准二维掺杂共价键轻质材料的研究，这些材料具有在环境压力下具有高温常规超导性的潜力。长期目标是筛选以形成共价框架的第二行元素为中心的大型组成空间，以及改善化合物稳定性的轻金属，该团队将首先对Li-M-B-C组成进行系统的从头开始探索。为了预测可合成的材料，该团队将使用先前开发的Ab-Initio结构进化模块（MAISE）平台，通过进化结构优化和机器学习原子间电位的结合，加速稳定化合物的鉴定。为了模拟超导特性，研究小组将依靠基于万尼尔函数的电子-声子万尼尔（EPW）代码，该代码可以在Eliashberg理论中解决超导各向异性。这项研究的一个组成部分是为超导体的合理设计开发基于物理的规则，这将通过寻找将易于计算的结构、电子和振动特性与超导特征相关联的描述符来实现。这项工作将为本科生和研究生提供一个获得先进电子结构方法、计算材料科学和高性能计算知识的机会。pi将继续组织研讨会和网络研讨会，向更广泛的材料研究界教授EPW和MAISE的基本理论和最佳使用。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。

Non-technical summaryThis EAGER award supports a joint computational and theoretical effort to guide the search for practical superconducting materials. Superconductors carry electrical current without any resistance when cooled down below a certain material-dependent critical temperature. This remarkable property has already found numerous applications, from maglev trains to the Large Hadron Collider, but present-day superconductors are difficult to manufacture or require ultra-low temperatures to function. New superconducting materials that can be mass-produced and operate at easily maintained temperatures have the potential to revolutionize energy, transportation, communication, and other emerging technologies.Design of new superconductors is notoriously difficult, because their properties are sensitive to the chemical composition and crystal structure. In this project, the team will focus on exploring promising combinations of light abundant elements including boron, carbon, and various metals. The PIs will employ advanced modeling methods and computational tools developed in their groups to identify and analyze suitable candidate materials. The search for stable compounds will be performed with a combination of an evolutionary algorithm and machine-learning interatomic potentials. Viable compounds will be examined with a computational method based on Wannier functions, a state-of-the-art approach for predicting superconducting properties.The PIs will contribute to the development of the next generation of scientists by organizing outreach activities for elementary-school students and involving students from underrepresented groups into STEM research. All new computational features added to the team's open-source packages will be made publicly available.Technical summaryThis EAGER award supports research aiming to identify quasi-two-dimensional doped-covalent-bond light-weight materials with potential for high-temperature conventional superconductivity at ambient pressure. With the long-term goal of screening a large compositional space centered on second-row elements forming covalent frameworks and light metals improving the compounds' stability, the team will first perform a systematic ab-initio exploration of the Li-M-B-C compositions. For predicting synthesizable materials, the team will use the previously developed Module for Ab-Initio Structure Evolution (MAISE) platform to accelerate the identification of stable compounds with a combination of evolutionary structure optimization and machine-learning interatomic potentials. For modeling superconducting properties, the team will rely on the Electron-Phonon Wannier (EPW) code based on Wannier functions, which enables resolving superconducting anisotropy within the Eliashberg theory. An integral part of the research is the development of physics-based rules for the rational design of superconductors, which will be achieved by finding descriptors correlating easy-to-calculate structural, electronic, and vibrational properties with superconducting features.The proposed work will offer an opportunity for undergraduate and graduate students to acquire knowledge in advanced electronic-structure methods, computational materials science, and high-performance computing. The PIs will continue to organize workshops and webinars to teach the underlying theory and optimal usage of EPW and MAISE to the broader materials-research community.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.