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。高温超导有望给能源的生产、储存和分配带来革命性的变化。尽管在铜和铁基材料中发现了非常规的高温超导，但已知的超导体不能被广泛开发，因为它们在非常低的温度下变成超导。由于对导致这些材料家族超导的必要成分缺乏共识，新超导体的发现和提高其转变温度一直受到限制。DMREF项目旨在利用已知的铜和铁基超导体的特征(描述符)作为设计标准，以寻找新的常压超导材料。有希望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本科生提供教育机会。技术摘要：DMREF项目将通过一个结合材料合成、局部探测和计算经验的多学科团队，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)和动力学平均场理论(DMFT)在内的计算工具来预测候选关联电子材料的晶体和电子结构。然后，最有希望的预测材料将通过结合两种传统上不同的材料合成形式来合成--反应氧化物分子束外延的先进薄膜沉积和低温软化学--以制造以前无法实现的亚稳定材料。然后，材料将被整体和局部探测器询问，以绘制电阻率、结构、轨道极化/杂化、声子和自旋相互作用的图。我们的方法结束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是人工智能就绪的，可以关联起来建立结构-财产关系。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.