Collaborative Research: DMREF: Discovery of unconventional superconductors by design

非技术摘要：超导是一种量子现象，电流在临界温度以下以零电阻流动。高温超导有望彻底改变能源的产生、储存和分配。尽管在铜基和铁基材料中发现了非常规的高温超导性，但已知的超导体不能被广泛利用，因为它们在非常低的温度下就会变成超导性。发现新的超导体并提高它们的转变温度，由于对这些材料家族中产生超导性的必要成分缺乏共识而受到限制。这个DMREF项目旨在利用已知的铜基和铁基超导体的特性（描述符）作为设计标准，以寻找新的常压超导材料。有前途的材料将在理论和实验之间的紧密反馈回路中合成。该项目还将为高中生和刚开始本科学习的学生提供教育机会。技术摘要：该DMREF项目将通过一个多学科团队，结合材料合成、局部探针和计算方面的经验，加速发现新的超导材料。为了设计和模拟新的超导体，本研究将使用包括密度泛函理论（DFT）和动态平均场理论（DMFT）在内的计算工具来预测候选相关电子材料的晶体和电子结构。最具前景的预测材料将通过结合两种传统上完全不同的材料合成形式——反应性氧化物分子束外延的高级薄膜沉积和低温软化学——来合成以前无法实现的亚稳态材料。然后，材料将被大块和局部探针询问，以绘制电阻率，结构，轨道极化/杂化，声子和自旋相互作用。我们的方法闭合了用我们的高温超导描述符预测材料和直接测量这些性质之间的循环。由此产生的大型数据集将为人工智能做好准备，并可以关联以建立结构-属性关系。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 abstract: Superconductivity is a quantum phenomenon where electricity flows with zero resistance below a critical temperature. High temperature superconductivity has the promise to revolutionize energy generation, storage, and distribution. Despite the discoveries of unconventional, high-temperature superconductivity in copper and iron-based materials, known superconductors cannot be widely exploited as they become superconducting at very low temperatures. Discovering new superconductors and increasing their transition temperatures has been limited by the lack of consensus about the necessary ingredients that give rise to superconductivity in these families of materials. This DMREF project seeks to exploit the characteristics (descriptors) of the known copper- and iron-based superconductors as design criteria in the search for new ambient-pressure superconducting materials. Promising materials will be synthesized in a tight feedback loop between theory and experiment. This project will also provide educational opportunities for students in high school and at the beginning of their undergraduate studies.Technical Abstract: This DMREF project will accelerate the discovery of new superconducting materials through a multi-disciplinary team that unites experience in materials synthesis, local probes, and computation. To design and simulate the new superconductors this research will use computational tools including density functional theory (DFT) and dynamical mean field theory (DMFT) to predict both the crystal and electronic structures of candidate correlated electron materials. The most promising predicted materials will then be synthesized by combining two traditionally disparate forms of materials synthesis -- advanced thin film deposition by reactive oxide molecular beam epitaxy and low-temperature soft chemistry -- to make metastable materials that could not previously be realized. The materials will then be interrogated by bulk and local probes to map resistivity, structure, orbital polarization/hybridization, phonons, and spin interactions. Our approach closes the loop between the prediction of materials with our high temperature superconductivity descriptors and direct measurement of these properties. The resulting large datasets will be Artificial Intelligence-ready and can be correlated to build structure-property relationships.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.