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: 1334713
• 负责人: Eric Toberer
• 金额: $ 85.6万
• 依托单位: Colorado School of Mines
• 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 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1334713&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****热电材料的发现是关键的瓶颈，限制了热电发电机在能量收集中广泛使用。 迄今为止，由于必须同时满足的多种相互冲突的财产要求，因此对此类材料的搜索一直在挑战。拟议的研究通过对材料进行的高通量搜索来解决这些挑战，这是由于大规模计算的持续改进以及适用于高通量计算的热电性能度量指标的开发而实现的。 电子结构和多数载体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，将开发用于理论预测的热电材料的快速实验验证方法。最终的实验/理论属性数据库的即时数据挖掘将产生指向新目标材料的材料 - 特质关系。 最终的技术和软件工具将是有据可查的和开放访问的。所得的性质数据库将作为一个长期中央，开放式存储库的种子，用于热电材料。 该研究计划为热电材料研究中一种新的，计算驱动的，范式的范式奠定了基础。太阳热电发电机和废热回收可以提供我们的电力需求的很大一部分。 该计划将导致发展和传播一种变革性方法，以实现新的热电材料，这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进行连续测试。 自适应数据挖掘将用于提取结构 - 特质趋势并有机地增长材料数据库。 这样一来，将培训新一代学生（社区学院，本科，研究生，结束后），通过将他们浸入完全综合的研究计划中，这是对理论和实验性科学方法的介绍。 这项努力通过研究小组的核心学生超越了专注于热电材料的综合理论/实验方法的研讨会，并在“大数据”环境中工作。 K-12和社区大学外展计划的套件针对STEM中代表性不足的群体的招募。 这些创新的计划包括教师培训模块，毕业课程和社区大学生的夏季研究机会。该奖项得到了材料研究（DMR），数学科学（DMS）以及计算机和网络系统（CNS）的支持。

项目成果

Polycrystalline ZrTe5 Parametrized as a Narrow-Band-Gap Semiconductor for Thermoelectric Performance

• DOI: 10.1103/physrevapplied.9.014025
• 发表时间: 2018-01-24
• 期刊: PHYSICAL REVIEW APPLIED
• 影响因子: 4.6
• 作者: Miller, Samuel A.;Witting, Ian;Snyder, G. Jeffrey
• 通讯作者: Snyder, G. Jeffrey

Matminer: An open source toolkit for materials data mining

• DOI: 10.1016/j.commatsci.2018.05.018
• 发表时间: 2018-09-01
• 期刊: COMPUTATIONAL MATERIALS SCIENCE
• 影响因子: 3.3
• 作者: Ward, Logan;Dunn, Alexander;Jain, Anubhav
• 通讯作者: Jain, Anubhav