Collaborative Research: DMREF: Accelerated Data-Driven Discovery of Ion-Conducting Materials

合作研究:DMREF:加速数据驱动的离子导电材料发现

基本信息

  • 批准号:
    2118838
  • 负责人:
  • 金额:
    $ 90万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-10-01 至 2025-09-30
  • 项目状态:
    未结题

项目摘要

NON-TECHNICAL SUMMARYOxides with fast ion-conduction are crucial components for a wide range of applications including batteries and solid-oxide fuel cells, which are needed for societal adoption of renewable energy technologies. However, progress in the research and development of ion-conducting ceramics has been sluggish, as time-consuming synthesis and sintering act as a bottleneck to new materials discovery. The project team will leverage their ultra-high-temperature synthesis technique that can rapidly sinter oxide materials in about 10 seconds, integrated with computational modeling and high-throughput measurements, to accelerate the discovery and design of novel oxide materials. The integrated closed-loop framework will advance a general paradigm for materials design and discovery in a fraction of the time of conventional discovery. Through this project, novel sodium-ion conducting materials will be discovered, which can be used for sodium batteries as economic, environmental-friendly, and sustainable alternatives to lithium-ion batteries for renewable energy storage. In addition, this project will leverage the interdisciplinary research program to create unique educational opportunities for a diverse group of graduate, undergraduate, K-12 students, and under-represented minorities. TECHNICAL SUMMARYThis project will integrate high-temperature rapid synthesis of ceramics with first-principles data-driven computation, high-throughput measurements, materials characterization, and microstructural modeling into a closed-loop framework to significantly accelerate the discovery and design of new ceramic oxide materials using sodium-ion conductors as model systems. The integrated closed-loop approach will advance an effective and general paradigm that comprehensively considers the complex interdependence among composition, sintering, microstructure, and properties for materials design and discovery in a fraction of the time of conventional discovery. The project will lead to improved understanding of the composition-sintering-microstructure-property relationships for a wide range of oxide materials, which will be of scientific value for guiding future research of new oxides. Education and outreach activities will be developed and undertaken in conjunction with the proposed research activities. In the spirit of Materials Genome Initiative (MGI), the education and outreach efforts will emphasize the unique components of data-driven closed-loop materials design as essential training for the next-generation MGI workforce.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.
非技术概述具有快速离子传导的氧化物是广泛应用的关键组分,包括电池和固体氧化物燃料电池,这是社会采用可再生能源技术所需的。然而,离子导电陶瓷的研究和开发进展缓慢,因为耗时的合成和烧结成为新材料发现的瓶颈。该项目团队将利用他们的超高温合成技术,该技术可以在大约10秒内快速烧结氧化物材料,并与计算建模和高通量测量相结合,以加速新型氧化物材料的发现和设计。集成的闭环框架将在传统发现的一小部分时间内推进材料设计和发现的一般范式。通过该项目,将发现新型钠离子导电材料,可用于钠电池,作为可再生能源存储锂离子电池的经济,环保和可持续替代品。此外,该项目将利用跨学科研究计划,为研究生,本科生,K-12学生和代表性不足的少数民族的多样化群体创造独特的教育机会。该项目将陶瓷的高温快速合成与第一原理数据驱动计算、高通量测量、材料表征和微观结构建模集成到一个闭环框架中,以显著加速使用钠离子导体作为模型系统的新型陶瓷氧化物材料的发现和设计。集成的闭环方法将推进一种有效和通用的范式,该范式全面考虑了材料设计和发现的组成,烧结,微观结构和性能之间的复杂相互依赖关系,而传统发现的时间只有一小部分。该项目将导致更好地理解各种氧化物材料的组成-烧结-微观结构-性能关系,这将对指导未来新氧化物的研究具有科学价值。将结合拟议的研究活动制定和开展教育和外联活动。本着材料基因组计划(MGI)的精神,教育和推广工作将强调数据驱动的闭环材料设计的独特组成部分,作为下一代MGI员工的基本培训。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

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Yifei Mo其他文献

A cost-effective all-in-one halide material for all-solid-state batteries
一种用于全固态电池的具有成本效益的一体化卤化物材料
  • DOI:
    10.1038/s41586-025-09153-1
  • 发表时间:
    2025-06-25
  • 期刊:
  • 影响因子:
    48.500
  • 作者:
    Jiamin Fu;Changhong Wang;Shuo Wang;Joel W. Reid;Jianwen Liang;Jing Luo;Jung Tae Kim;Yang Zhao;Xiaofei Yang;Feipeng Zhao;Weihan Li;Bolin Fu;Xiaoting Lin;Yang Hu;Han Su;Xiaoge Hao;Yingjie Gao;Shutao Zhang;Ziqing Wang;Jue Liu;Hamid Abdolvand;Tsun-Kong Sham;Yifei Mo;Xueliang Sun
  • 通讯作者:
    Xueliang Sun
Safety information on transgenic plants expressing Bacillus thuringiensis-Derived insect control protein
表达苏云金芽孢杆菌衍生昆虫控制蛋白的转基因植物的安全信息
  • DOI:
    10.1787/oecd_papers-v7-art35-en
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    9.4
  • 作者:
    Yunsheng Liu;Yifei Mo
  • 通讯作者:
    Yifei Mo
Superionic conducting vacancy-rich β-Li3N electrolyte for stable cycling of all-solid-state lithium metal batteries
用于全固态锂金属电池稳定循环的富空位超离子导电β-Li3N 电解质
  • DOI:
    10.1038/s41565-024-01813-z
  • 发表时间:
    2024-11-25
  • 期刊:
  • 影响因子:
    34.900
  • 作者:
    Weihan Li;Minsi Li;Shuo Wang;Po-Hsiu Chien;Jing Luo;Jiamin Fu;Xiaoting Lin;Graham King;Renfei Feng;Jian Wang;Jigang Zhou;Ruying Li;Jue Liu;Yifei Mo;Tsun-Kong Sham;Xueliang Sun
  • 通讯作者:
    Xueliang Sun
Transition of nc-SiC powder surface into grain boundaries during sintering by molecular dynamics simulation and neutron powder diffraction
通过分子动力学模拟和中子粉末衍射研究烧结过程中 nc-SiC 粉末表面向晶界的转变
  • DOI:
    10.1524/zkri.2007.2007.suppl_26.255
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Marcin Wojdyr;Yifei Mo;E. Grzanka;S. Stelmakh;S. Gierlotka;T. Proffen;T. W. Żerda;B. Palosz;I. Szlufarska
  • 通讯作者:
    I. Szlufarska
Association between gestational hypnotic benzodiazepine receptor agonists exposure and adverse pregnancy outcomes: a systematic review and meta-analysis
  • DOI:
    10.1007/s00737-024-01516-3
  • 发表时间:
    2024-09-24
  • 期刊:
  • 影响因子:
    2.700
  • 作者:
    Xinyuan Wang;Jun Xu;Yifei Mo;Linrun Wang
  • 通讯作者:
    Linrun Wang

Yifei Mo的其他文献

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{{ truncateString('Yifei Mo', 18)}}的其他基金

Collaborative Research: Guiding synthesis of nanoparticles with nanometric phase diagram and in situ X-ray diffraction
合作研究:用纳米相图和原位X射线衍射指导纳米颗粒的合成
  • 批准号:
    2004837
  • 财政年份:
    2020
  • 资助金额:
    $ 90万
  • 项目类别:
    Standard Grant
Collaborative Research: Integrating Physics and Generative Machine-Learning Models for Inverse Materials Design
合作研究:整合物理和生成机器学习模型进行逆向材料设计
  • 批准号:
    1940166
  • 财政年份:
    2019
  • 资助金额:
    $ 90万
  • 项目类别:
    Continuing Grant
SI2-SSI: Collaborative Research: A Robust High-Throughput Ab Initio Computation and Analysis Software Framework for Interface Materials Science
SI2-SSI:协作研究:用于界面材料科学的强大高通量从头计算和分析软件框架
  • 批准号:
    1550423
  • 财政年份:
    2016
  • 资助金额:
    $ 90万
  • 项目类别:
    Standard Grant

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    10774081
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  • 项目类别:
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