DMREF: Collaborative Research: Accelerating Thermoelectric Materials Discovery via Dopability Predictions

DMREF：协作研究：通过可掺杂性预测加速热电材料的发现

• 批准号: 1729594
• 负责人: Eric Toberer
• 金额: $ 95.9万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1729594&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Accelerating; Thermoelectric

Non-technical Description: Thermoelectric devices, which transform heat flow into electrical power and vice versa, have the potential to revolutionize how society produces electricity and cooling. However, thermoelectric materials suffer from poor power conversion efficiency and the search continues for new materials with enhanced performance. In this project, advances in computation and machine learning are leveraged to accelerate this search for advanced thermoelectric materials. These efforts build upon the prior NSF DMREF research of some of team members on predicting a material's potential for thermoelectric performance. High throughput screening focused on identifying semiconductors with desirable electronic and vibrational properties. However, these efforts did not include a strong focus on the role of intrinsic defect or the potential for dopability. In the next stage of this research, these critical components will be pursued through a mixture of high throughput theory, experimental validation, and machine learning. Together, these efforts will yield accurate prediction of the thermoelectric potential for thousands of semiconductors and the realization of new materials for solid state power generation. Beyond thermoelectric materials, these efforts to establish a dopability recommendation engine will be critical in the development of next generation microelectronic and optoelectronic materials such as transparent conductors and photovoltaic absorbers. Technical Description: The project's ultimate objective is to build a robust and accurate dopability recommendation engine to overcome the dopability bottleneck in thermoelectric materials discovery. The recommendation engine will use materials informatics to enable high-throughput predictions of dopability, relying only on quantities that are inexpensive to calculate, experimental measurements, and known structural/chemical features as inputs. It will thus allow dopability screening of thousands of compounds. First, an accurate training set will be built for the recommendation engine containing native defect formation enthalpies and structural/chemical descriptors from a diverse array of thermoelectric-relevant compounds. Whereas prior dopant studies focused on single compounds, a new, automated calculation infrastructure will be leveraged that allows the rapid creation of an extensive training set, initially containing approximately 30 compounds but growing to over 100 during the project. Experimental charge transport and local dopant structure measurements will validate the training set. Second, the prediction engine will be trained on the data to extract patterns and correlations, and ultimately identify robust descriptors of dopability. Initially, the engine will predict if `killer' defects limit the available dopant range. The engine will ultimately grow to suggest specific extrinsic dopants for compounds that pass this initial screening. Together, this combination of accurate predictions of intrinsic transport properties (prior DMREF) and dopability (proposed DMREF) is expected to accelerate the discovery process for thermoelectric materials.

非技术描述：热电装置，将热流转化为电能，反之亦然，有可能彻底改变社会生产电力和冷却的方式。然而，热电材料的功率转换效率较差，因此对性能增强的新材料的研究仍在继续。在这个项目中，计算和机器学习的进步被用来加速对先进热电材料的研究。这些努力建立在先前NSF DMREF研究的基础上，一些团队成员预测了材料的热电性能潜力。高通量筛选侧重于识别具有理想电子和振动特性的半导体。然而，这些努力并没有包括对内在缺陷的作用或可移植性的潜在关注。在这项研究的下一阶段，这些关键组成部分将通过高通量理论、实验验证和机器学习的混合来追求。总之，这些努力将为数千种半导体提供准确的热电势预测，并实现固态发电的新材料。除了热电材料之外，这些建立可操作性推荐引擎的努力将对下一代微电子和光电子材料（如透明导体和光伏吸收器）的发展至关重要。技术描述：该项目的最终目标是建立一个强大而准确的可dopability推荐引擎，以克服热电材料发现中的可dopability瓶颈。推荐引擎将使用材料信息学来实现高通量的可操作性预测，仅依赖于计算成本低廉的数量、实验测量和已知的结构/化学特征作为输入。因此，它将允许对数千种化合物进行可行性筛选。首先，将为推荐引擎构建一个精确的训练集，其中包含来自各种热电相关化合物的天然缺陷形成焓和结构/化学描述符。鉴于之前的掺杂剂研究主要集中在单一化合物上，一种新的自动化计算基础设施将被利用，允许快速创建广泛的训练集，最初包含大约30种化合物，但在项目期间增长到100多种。实验电荷输运和局部掺杂结构测量将验证训练集的有效性。其次，预测引擎将在数据上进行训练，以提取模式和相关性，并最终识别可操作性的鲁棒描述符。最初，发动机将预测“致命”缺陷是否限制了可用的掺杂范围。该引擎最终将为通过初步筛选的化合物提供特定的外部掺杂剂。总之，这种准确预测本征输运性质（先前的DMREF）和可dopability（提议的DMREF）的结合有望加速热电材料的发现过程。

项目成果

Ultralow Thermal Conductivity in Diamond-Like Semiconductors: Selective Scattering of Phonons from Antisite Defects

类金刚石半导体中的超低导热率：反位缺陷选择性散射声子

• DOI: 10.1021/acs.chemmater.8b00890
• 发表时间: 2018
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: Ortiz, Brenden R.;Peng, Wanyue;Gomes, Lídia C.;Gorai, Prashun;Zhu, Taishan;Smiadak, David M.;Snyder, G. Jeffrey;Stevanović, Vladan;Ertekin, Elif;Zevalkink, Alexandra
• 通讯作者: Zevalkink, Alexandra

The Thermoelectric Properties of Bismuth Telluride

• DOI: 10.1002/aelm.201800904
• 发表时间: 2019-06-01
• 期刊: ADVANCED ELECTRONIC MATERIALS
• 影响因子: 6.2
• 作者: Witting, Ian T.;Chasapis, Thomas C.;Snyder, G. Jeffrey
• 通讯作者: Snyder, G. Jeffrey

Extrinsic doping of Hg 2 GeTe 4 in the face of defect compensation and phase competition

Hg 2 GeTe 4 的外在掺杂面临缺陷补偿和相位竞争

• DOI: 10.1039/d3tc00209h
• 发表时间: 2023
• 期刊: Journal of Materials Chemistry C
• 影响因子: 6.4
• 作者: Porter, Claire E.;Qu, Jiaxing;Cielsielski, Kamil;Ertekin, Elif;Toberer, Eric S.
• 通讯作者: Toberer, Eric S.

Empirical modeling of dopability in diamond-like semiconductors

• DOI: 10.1038/s41524-018-0123-6
• 发表时间: 2018-12
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: Samuel A. Miller;M. Dylla;Shashwat Anand;Kiarash Gordiz;G. J. Snyder;E. Toberer

The importance of phase equilibrium for doping efficiency: iodine doped PbTe

• DOI: 10.1039/c9mh00294d
• 发表时间: 2019-08-01
• 期刊: MATERIALS HORIZONS
• 影响因子: 13.3
• 作者: Male, James;Agne, Matthias T.;Snyder, G. Jeffrey
• 通讯作者: Snyder, G. Jeffrey