DMREF/Collaborative Research: Computationally Driven Targeting of Advanced Thermoelectric Materials

DMREF/合作研究：计算驱动的先进热电材料靶向

• 批准号: 1333335
• 负责人: Scott Barnett
• 金额: $ 36万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1333335&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Computationally; Driven

****Technical Abstract****The discovery of thermoelectric materials is the critical bottleneck limiting the widespread use of thermoelectric generators for energy harvesting. To date, the search for such materials has been challenging due to the multitude of conflicting property requirements that must be simultaneously satisfied. The proposed research addresses these challenges through a high-throughput search for materials, enabled by the continued improvements in large-scale computing and the development of a thermoelectric performance metric suitable for high-throughput calculations. High accuracy measurements of electronic structure and majority carrier transport properties will be used to validate the calculated descriptors. In support of these efforts, rapid experimental validation approaches for theory-predicted thermoelectric materials will be developed. On-the-fly data mining of the resulting experimental/theoretical property database will yield material-property relationships pointing to new target materials. The resulting techniques and software tools will be well-documented and open-access. The resulting property database will serve as the seed for a long-term central, open repository for thermoelectric materials. This research program lays the groundwork for a new, computationally driven, paradigm in thermoelectric material research.****Non-Technical Abstract****The development of advanced thermoelectric materials could have a profound impact on the nation's energy portfolio. Solar thermoelectric generators and waste heat recovery could provide a significant fraction of our electricity needs. This program will lead to the development and dissemination of a transformative methodology for the realization of new thermoelectric materials, which can be extended to other materials sub-disciplines. High throughput electronic structure calculations of known earth-abundant compounds will provide the critical descriptors to identify new materials. The veracity of these calculations will be continuously tested through experimental measurements. Adaptive data mining will be used to extract structure-property trends and organically grow the material database. In doing so, a new generation of students (community college, undergraduate, graduate, post-doc) will be trained, which are conversant with both theoretical and experimental approaches to science by immersing them in a fully integrated research program. This effort extends beyond the core students in the research group through workshops focused on integrated theory/experiment approaches to thermoelectric materials and working in a "big-data" environment. A suite of K-12 and community college outreach programs targets the recruitment of underrepresented groups in STEM. These innovative programs include teacher training modules, after school programs, and summer research opportunities for community college students.This award is supported by the Divisions of Materials Research (DMR), of Mathematical Sciences (DMS), and of Computer and Network Systems (CNS).

****技术摘要****热电材料的发现是限制热电发电机广泛用​​于能量收集的关键瓶颈。 迄今为止，由于必须同时满足多种相互冲突的性能要求，寻找此类材料一直具有挑战性。拟议的研究通过大规模计算的持续改进和适合高通量计算的热电性能指标的开发，通过对材料的高通量搜索来解决这些挑战。 电子结构和多数载流子传输特性的高精度测量将用于验证计算的描述符。为了支持这些努力，将开发理论预测热电材料的快速实验验证方法。对所得实验/理论特性数据库的动态数据挖掘将产生指向新目标材料的材料-特性关系。 由此产生的技术和软件工具将有详细记录并开放获取。由此产生的特性数据库将作为热电材料长期中央开放存储库的种子。 该研究计划为热电材料研究的新的、计算驱动的范例奠定了基础。****非技术摘要****先进热电材料的开发可能对国家的能源组合产生深远的影响。太阳能热电发电机和废热回收可以满足我们电力需求的很大一部分。 该计划将导致开发和传播用于实现新型热电材料的变革性方法，该方法可以扩展到其他材料子学科。 已知地球丰富化合物的高通量电子结构计算将为识别新材料提供关键描述符。这些计算的准确性将通过实验测量不断得到检验。 自适应数据挖掘将用于提取结构-性能趋势并有机地增长材料数据库。 在此过程中，新一代学生（社区学院、本科生、研究生、博士后）将接受培训，通过沉浸在完全综合的研究项目中，熟悉科学的理论和实验方法。 通过专注于热电材料综合理论/实验方法和在“大数据”环境中工作的研讨会，这项工作超出了研究小组的核心学生的范围。 一系列 K-12 和社区大学外展计划的目标是招募 STEM 领域代表性不足的群体。 这些创新项目包括教师培训模块、课外项目以及社区学院学生的暑期研究机会。该奖项由材料研究部 (DMR)、数学科学部 (DMS) 以及计算机和网络系统部 (CNS) 的支持。