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*热电材料的发现是限制热电发电机广泛用于能量收集的关键瓶颈。到目前为止，寻找这类材料一直是具有挑战性的，因为必须同时满足大量相互冲突的性能要求。拟议的研究通过对材料的高通量搜索来解决这些挑战，这得益于大规模计算的持续改进和适用于高通量计算的热电性能指标的开发。将使用电子结构和大多数载流子输运性质的高精度测量来验证计算的描述符。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。对所产生的实验/理论属性数据库进行即时数据挖掘将产生指向新目标材料的材料-属性关系。由此产生的技术和软件工具将是有据可查的和开放获取的。由此产生的财产数据库将作为长期中央、开放的热电材料储存库的种子。这一研究计划为热电材料研究中一种新的、计算驱动的范例奠定了基础。*非技术摘要*先进热电材料的发展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余热回收可以提供我们电力需求的很大一部分。这项计划将导致开发和传播一种实现新热电材料的变革性方法，这种方法可以推广到其他材料分支学科。已知的富含地球的化合物的高通量电子结构计算将提供识别新材料的关键描述符。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-属性趋势，并有机地增长材料数据库。在这样做的过程中，新一代学生(社区大学、本科生、研究生、博士后)将得到培训，他们将通过沉浸在完全整合的研究计划中，熟悉科学的理论和实验方法。这项工作不仅限于研究组的核心学生，还通过研讨会，重点关注热电材料的综合理论/实验方法，并在“大数据”环境中工作。一套K-12和社区大学外展计划的目标是招募STEM中代表性不足的群体。该奖项由材料研究(DMR)、数学科学(DMS)和计算机与网络系统(CNS)分部支持。

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