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.

非技术摘要这一奖项支持理论研究和教育，旨在推进超导体的计算设计。超导材料在技术上很重要，因为电流可以流经它们而不会损失能量，并且因为它们的内部驱逐了磁场。这些特性使超导体能够在SmartGrid项目，悬浮火车以及用于磁共振成像（MRI）机器和风盘用于的电磁体中用作电缆。不幸的是，所有在技术上有用的超导体都必须冷却至非常低的温度，低于液氮沸腾的寒冷温度。在室温下寻找作为超导体的材料将彻底改变电基础设施，医疗保健，并以难以想象的方式影响我们的生活。富含氢的固体是该项目的重点，因为研究表明它们可以在高温下作为超导体行为。就可以在地球深处高压下合成钻石，研究人员可能会改变压力以创建具有异常特性的新材料。最近以这种方式合成了几种超导体，表现出接近室温的超导性发作。其中许多是在计算上预测的，并且可以通过设计成功案例成为材料。 PI将执行量子机械计算，以预测有希望的新超导靶标的合成目标，并将与高压研究中的领先实验组合作，以尝试创建这些材料。这项工作的重点是发现在温度高的室温下，在较低的最高温度超导材料下，它们是室温高的材料。为了促进这一目标，PI将进一步开发可以预测固体结构而无需任何实验信息的软件。机器学习方法将与晶体结构预测软件相连，以加速计算。最终的计划将免费提供给材料科学，物理和化学社区，从而促进了理性材料设计的进步以及科学和工程学的当前和未来发现。作为该项目的一部分，将对计算材料发现进行培训。为了扩大他们的参与，来自代表性不足小组的本科生将通过人事交流进行计算建模和材料预测，为STEM领域的未来职业机会铺平道路。技术摘要奖支持理论和计算研究和教育，这些奖项将导致新颖的氢化高级富裕超级负责人的合理设计。 PI将在计算上以独特的化学计量和结构来预测可以在压力下合成的独特化学计量和结构的晶体结构，并通过第一原理计算研究其电子结构和特性。重点将放在计算上绘制三元氢化物的相图作为压力的函数。这些系统目前正在接受激烈的调查，因为研究表明它们可能在更高的温度或压力下以比最近对二进制氢化物进行较低的压力行为。在高压研究中，领先的实验组将确认计算预测。Xtalopt进化算法将进化算法仅在仅在其化学计量的情况下来预测扩展系统的结构。机器学习方法将加速晶体结构搜索的进度并将其集中在可能具有最高超导临界温度的材料上，并将与Xtalopt连接。众所周知的化学构建器，编辑器和可视化器Avogadro中的晶体学套件将进一步提高。 Xtalopt和Avogadro是开源软件，它们的发展有助于创建网络基础设施，促进了科学和工程学中的当前和未来发现。研究生和本科生将接受理性计算材料的培训，从而为未来的职业做好准备，以使他们为理论，计算和经验的未来职业做好准备。与学生和教师交流涉及的主要本科，少数派服务机构的合作将使学生从代表性不足的群体中揭露学生在STEM领域的研究和未来的职业机会，并通过第一原告建模技术进行培训。该奖项反映了NSF的法定任务，并通过评估基金会的智力效果和广泛的范围来反映出值得评估的支持。

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