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

该奖项支持旨在用先进建模方法设计超导材料的计算研究。超导体在冷却到某一临界温度以下时表现出一种独特的特性，即导电无电阻。利用环境压力技术和高温超导技术合成的新材料的发现可以影响能源存储和分配、医药、电子和运输领域的大量新兴技术。该项目将涉及一个巨大的组成和结构空间的系统筛选。化学组合将包括可以形成强键层状框架的轻质元素和可以使共价框架稳定和超导的不同金属。为了研究和调整候选材料的关键属性，该团队将向在pi小组中开发的软件包中添加新的功能。这些新特性将使使用人工智能方法研究大规模现象和使用尖端电子结构方法评估复杂材料的超导特性成为可能。教育活动将侧重于培养计算材料科学和高性能计算方面的研究生和本科生。该团队还将参与面向K-12学生的拓展活动，以帮助从代表性不足的群体中吸引新一代科学家进入科学、技术、工程和数学学科。所有新的计算特性将免费提供给更广泛的物理学家、化学家和材料科学家。技术概述：该奖项支持一个关于预测可在环境压力下合成的高温超导体的合作项目。该团队的探索性工作已经确定层状金属硼碳化物是一种很有前途的材料类别，可以承载具有目标电子和振动特性的新合成化合物。与搜索基态晶体结构（可以用各种现有算法执行）相比，识别与温度和成分相关的合成路线产生亚稳态材料是一项艰巨得多的任务。该团队将采用从头算方法和机器学习原子间势相结合的方法来探索可能导致所需亚稳态配置的复杂动力学保护途径。所产生的结构的大尺寸和可能的无序性将使其超导性质的准确描述成为一个相当大的挑战。pi将引入电子-声子耦合的新描述符和各向异性Migdal-Eliashberg框架内的新功能，以实现对候选材料超导临界温度的高通量评估。pi的电子结构软件包（MAISE和EPW）中的新功能将通过成熟的平台在开源GNU通用公共许可证下传播，并在研讨会上展示，以确保科学界将及时从这些发展中受益。这些pi还将在计算材料物理学和高性能计算方面培训研究生，并通过宾厄姆顿大学物理拓展计划组织的互动演示向K-12学生介绍当今的材料研究。这些努力旨在培养年轻一代对STEM学科的兴趣，将有助于培养一支技术熟练的劳动力队伍，以推进网络基础设施和计算材料的研究。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。

NONTECHNICAL SUMMARYThis award supports computational research aimed at designing superconducting materials with advanced modeling methods. Superconductors display a unique property of conducting electrical current without any resistance when cooled below a certain critical temperature. Discovery of new materials synthesizable with ambient-pressure techniques and superconducting at high temperatures can impact a wealth of emerging technologies in the areas of energy storage and distribution, medicine, electronics, and transportation.The project will involve a systematic screening of a vast compositional and structural space. The chemical set will include light-weight elements that can form strongly bonded layered frameworks and different metals that can make the covalent frameworks stable and superconducting. In order to study and tune the key properties of candidate materials, the team will add new capabilities to software packages developed in the PIs’ groups. The new features will enable investigation of large-scale phenomena using artificial intelligence approaches and evaluation of the complex materials’ superconducting properties with cutting-edge electronic structure methods.The educational activities will focus on training graduate and undergraduate students in computational materials science and high-performance computing. The team will also participate in outreach activities for K-12 students to help attract a new generation of scientists from underrepresented groups into the Science, Technology, Engineering, and Mathematics disciplines. All new computational features will be made freely available to reach a wider community of physicists, chemists, and materials scientists.TECHNICAL SUMMARYThis award supports a collaborative project on the prediction of high-temperature superconductors that can be synthesized at ambient pressure. The team’s exploratory work has identified layered metal borocarbides as a promising materials class to host new synthesizable compounds with targeted electronic and vibrational properties. In contrast to searches for ground state crystal structures that can be performed with a variety of existing algorithms, identification of temperature- and composition-dependent synthesis routes yielding metastable materials is a far more difficult task. The team will employ a combination of ab initio methods and machine learning interatomic potentials to explore complex kinetics-protected pathways that may lead to the desired metastable configurations. The large size and possible disorder of the resulting structures will make the accurate description of their superconducting properties a considerable challenge. The PIs will introduce new descriptors of the electron-phonon coupling and new capabilities within the anisotropic Migdal-Eliashberg framework to enable a high-throughput evaluation of the candidate materials’ superconducting critical temperatures.The new features in the PIs’ electronic structure software packages (MAISE and EPW) will be disseminated under the open-source GNU General Public License via well-established platforms and presented at workshops to ensure that the scientific community will benefit from these developments in a timely fashion. The PIs will also train (under)graduate students in computational materials physics and high-performance computing as well as introduce K-12 students to present-day materials research through interactive demonstrations organized with the help of the Physics Outreach Program at Binghamton University. Aimed at fostering the young generation’s interest in STEM disciplines, these efforts will contribute to the development of a skilled workforce for advancing cyberinfrastructure and computational materials research.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.