Theoretical Prediction of Hydrogen Rich High-Temperature Superconductors

富氢高温超导体的理论预测

• 批准号: 2136038
• 负责人: Eva Zurek
• 金额: $ 39.5万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136038&HistoricalAwards=false
• 关键词: Theoretical; Prediction; Hydrogen; Rich; Temperature

NONTECHNICAL SUMMARYThis award supports theoretical research and education aimed to advance computational design of superconductors. Superconducting materials are technologically important because electrical current can flow through them without energy loss, and because their interiors expel magnetic fields. These properties enable superconductors to be employed as cables in SmartGrid projects, in levitating trains, and as electromagnets used in Magnetic Resonance Imaging (MRI) machines and wind-turbines. Unfortunately, all the superconductors that are technologically useful must be cooled to very low temperatures, below the frigid temperature where liquid nitrogen boils. Finding materials that behave as superconductors at room temperature would revolutionize electrical infrastructure, health care, and impact our lives in unimaginable ways. Hydrogen-rich solids are the focus of this project because research suggests that they could behave as superconductors at high temperatures.Just as diamonds can be synthesized at high pressures deep within the Earth, researchers can vary pressure to create new materials with unusual properties. Several superconductors have recently been synthesized in this way, exhibiting superconductivity onset approaching room temperature. Many of these have been computationally predicted and could be materials by design success stories. The PI will carry out quantum mechanical calculations to predict promising new superconducting targets for synthesis and will collaborate with leading experimental groups in high-pressure research that will attempt to create these materials. A focus of the work is finding materials that are superconductors at temperatures as high as room temperature at lower pressures than current highest temperature superconducting materials. To advance this goal the PI will further develop software that can predict the structure of a solid without any experimental information. Machine learning methods will be interfaced with the crystal structure prediction software to accelerate the calculations. The resulting programs will be made freely available to the materials science, physics, and chemistry communities, facilitating the advance of rational materials design as well as current and future discoveries in science and engineering.Graduate and undergraduate students will be trained in computational materials discovery as part of this project. Aiming to broaden their participation, undergraduate students from underrepresented groups will be trained in computational modelling and materials prediction via personnel exchange, paving the way for future career opportunities in STEM fields.TECHNICAL SUMMARYThis award supports theoretical and computational research and education that will lead towards the rational design of novel hydrogen-rich superconductors. The PI will computationally predict the crystal structures of hydrides with unique stoichiometries and structures that can be synthesized under pressure and study their electronic structure and properties via first-principles calculations. The focus will be on computationally mapping out the phase diagrams of ternary hydrides as a function of pressure. These systems are currently under intense investigation, since research suggests they may behave as superconductors at higher temperatures or lower pressures than the binary hydrides that have been recently studied intensively. The computational predictions will be confirmed by leading experimental groups in high-pressure research.The XtalOpt evolutionary algorithm that can be used to predict the structure of an extended system given only its stoichiometry, will be further developed. Machine learning methods that will accelerate the progress of the crystal structure searches and focus them on materials that are likely to have the highest superconducting critical temperatures, will be interfaced with XtalOpt. The crystallography suite within the highly popular chemical builder, editor, and visualizer Avogadro, will be further advanced. XtalOpt and Avogadro are open-source software, and their development contributes to the creation of cyberinfrastructure, facilitating current and future discoveries in science and engineering.Graduate and undergraduate students will be trained in rational computational materials design and programming, thereby preparing them for future careers where synergy between theory, computation, and experiment leads to innovation. Collaboration with primarily undergraduate, minority-serving institutions that involves student and faculty exchange will expose students from underrepresented groups to research and future career opportunities in STEM fields and train them in first-principles modelling techniques.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.

非技术总结该奖项支持旨在推进超导体计算设计的理论研究和教育。超导材料在技术上很重要，因为电流可以在没有能量损失的情况下流过它们，并且因为它们的内部会排出磁场。这些特性使超导体能够用作智能电网项目中的电缆，悬浮列车，以及磁共振成像（MRI）机器和风力涡轮机中使用的电磁铁。不幸的是，所有在技术上有用的超导体都必须冷却到非常低的温度，低于液氮沸腾的寒冷温度。找到在室温下表现为超导体的材料将彻底改变电力基础设施，医疗保健，并以难以想象的方式影响我们的生活。富氢固体是该项目的重点，因为研究表明，它们在高温下可以表现出超导体的特性。正如钻石可以在地球深处的高压下合成一样，研究人员可以改变压力，创造出具有不同寻常特性的新材料。最近已经用这种方法合成了几种超导体，表现出接近室温的超导性。其中许多已经通过计算预测，并可能成为设计成功案例的材料。PI将进行量子力学计算，以预测有前途的新超导目标，并将与高压研究中的领先实验小组合作，试图创造这些材料。这项工作的一个重点是寻找在室温下高温超导的材料，而压力低于目前的最高温度超导材料。为了推进这一目标，PI将进一步开发可以在没有任何实验信息的情况下预测固体结构的软件。机器学习方法将与晶体结构预测软件接口，以加速计算。由此产生的程序将免费提供给材料科学，物理和化学社区，促进合理的材料设计以及当前和未来的科学和工程发现的进步。研究生和本科生将在计算材料发现作为该项目的一部分进行培训。为了扩大他们的参与，来自代表性不足群体的本科生将通过人员交流进行计算建模和材料预测方面的培训，为未来在STEM领域的职业机会铺平道路。技术总结该奖项支持理论和计算研究和教育，从而导致新型富氢超导体的合理设计。PI将通过计算预测具有独特化学计量和可在压力下合成的结构的双金属化合物的晶体结构，并通过第一性原理计算研究其电子结构和性质。重点将放在计算绘制出作为压力的函数的三元相图。这些系统目前正在进行深入的研究，因为研究表明它们可能在比最近深入研究的二元超导体更高的温度或更低的压力下表现为超导体。计算预测将得到高压研究领域领先实验小组的确认。XtalOpt进化算法将得到进一步开发，该算法可用于预测仅给定化学计量的扩展系统的结构。机器学习方法将加速晶体结构搜索的进展，并将其集中在可能具有最高超导临界温度的材料上，这些方法将与XtalOpt接口。非常受欢迎的化学构建器，编辑器和可视化器Avogadro中的晶体学套件将进一步发展。XtalOpt和Avogadro是开源软件，它们的开发有助于创建网络基础设施，促进当前和未来的科学和工程发现。研究生和本科生将接受理性计算材料设计和编程的培训，从而为未来的职业生涯做好准备，理论，计算和实验之间的协同作用导致创新。与主要为本科生、少数民族服务的机构合作，包括学生和教师交流，将使来自代表性不足群体的学生接触到STEM领域的研究和未来的职业机会，并培训他们掌握第一原理建模技术。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Applying Density Functional Theory to Common Organic Mechanisms: A Computational Exercise

• DOI: 10.1021/acs.jchemed.2c00935
• 发表时间: 2022-11
• 期刊: Journal of Chemical Education
• 影响因子: 3
• 作者: Jonathan P. Antle;Masashi W. Kimura;Stefano Racioppi;Corey Damon;Meredith Lang;Caitlyn M. Gatley-Montross;L. Sánchez B.;Daniel P. Miller;E. Zurek;Adam M. Brown;Kellie Gast;S. Simpson

A Computational Experiment Introducing Undergraduates to Geometry Optimizations, Vibrational Frequencies, and Potential Energy Surfaces

向本科生介绍几何优化、振动频率和势能面的计算实验

• DOI: 10.1021/acs.jchemed.2c01129
• 发表时间: 2023
• 期刊: Journal of Chemical Education
• 影响因子: 3
• 作者: Hanson, Matthew D.;Miller, Daniel P.;Kondeti, Cholavardhan;Brown, Adam;Zurek, Eva;Simpson, Scott
• 通讯作者: Simpson, Scott

Designing ternary superconducting hydrides with A15-type structure at moderate pressures

中等压力下A15型结构三元超导氢化物的设计

• DOI: 10.1016/j.mtphys.2023.101086
• 发表时间: 2023
• 期刊: Materials Today Physics
• 影响因子: 11.5
• 作者: Wei, Xudong;Hao, Xiaokuan;Bergara, Aitor;Zurek, Eva;Liang, Xiaowei;Wang, Linyan;Song, Xiaoxu;Li, Peifang;Wang, Lin;Gao, Guoying
• 通讯作者: Gao, Guoying