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: 1729149
• 负责人: Elif Ertekin
• 金额: $ 32万
• 依托单位: University of Illinois at Urbana-Champaign
• 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 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1729149&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：将热流转化为电力，反之亦然的热电设备有可能彻底改变社会如何产生电力和冷却。 但是，热电材料的功率转化效率较差，并且搜索继续寻找具有增强性能的新材料。在这个项目中，利用计算和机器学习的进步来加速此搜索高级热电材料。这些努力是基于一些团队成员的NSF DMREF研究，以预测材料的热电性能的潜力。高吞吐量筛选的重点是识别具有理想的电子和振动特性的半导体。 但是，这些努力并不包括对固有缺陷的作用或潜在可抑制性的作用。 在这项研究的下一个阶段，将通过高通量理论，实验验证和机器学习的混合来追求这些关键组成部分。 这些努力总之将对数千个半导体的热电潜力进行准确的预测，并实现新材料来产生固态发电。 除了热电材料之外，这些建立耐用性建议发动机的努力对于下一代微电子和光电材料（例如透明导体和光伏吸收器）的开发至关重要。 技术描述：该项目的最终目标是建立强大而准确的耐用性建议引擎，以克服热电材料发现中的可毒性瓶颈。该建议引擎将使用材料信息学来实现对障碍的高通量预测，仅依靠便宜的数量来计算，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衰老筛选。 首先，将为推荐引擎建造一个精确的训练集，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。尽管以前的掺杂剂研究集中在单个化合物上，但将利用一种新的自动计算基础架构，可以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多个。实验电荷运输和局部掺杂剂结构测量将验证训练集。其次，预测引擎将对数据进行培训，以提取模式和相关性，并最终确定可吸毒性的强大描述符。 最初，发动机将预测“杀手”缺陷是否限制了可用的掺杂量范围。发动机最终将增长，以建议通过此初始筛选的化合物的特定外部掺杂剂。 总之，预期对固有转运性能（先验DMREF）和可毒性（提议的DMREF）的准确预测的组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

Tuning valley degeneracy with band inversion

通过能带反转调节谷简并性

• DOI: 10.1039/d1ta08379a
• 发表时间: 2022
• 期刊: Journal of Materials Chemistry A
• 影响因子: 11.9
• 作者: Toriyama, Michael Y.;Brod, Madison K.;Gomes, Lídia C.;Bipasha, Ferdaushi A.;Assaf, Badih A.;Ertekin, Elif;Snyder, G. Jeffrey
• 通讯作者: Snyder, G. Jeffrey

Anomalous electronic properties in layered, disordered ZnVSb

• DOI: 10.1103/physrevmaterials.5.015002
• 发表时间: 2021-01
• 期刊: Physical Review Materials
• 影响因子: 3.4
• 作者: Erik A. Bensen;K. Ciesielski;L. C. Gomes;B. Ortiz;Johannes Falke;O. Pavlosiuk;D. Weber;Tara Braden-Ta

Mixed phononic and non-phononic transport in hybrid lead halide perovskites: glass-crystal duality, dynamical disorder, and anharmonicity

• DOI: 10.1039/c8ee02820f
• 发表时间: 2019-01
• 期刊: Energy & Environmental Science
• 影响因子: 32.5
• 作者: T. Zhu;E. Ertekin

Doping by design: finding new n-type dopable ABX 4 Zintl phases for thermoelectrics

通过设计掺杂：寻找用于热电的新型 n 型可掺杂 ABX 4 Zintl 相

• DOI: 10.1039/d0ta08238d
• 发表时间: 2020
• 期刊: Journal of Materials Chemistry A
• 影响因子: 11.9
• 作者: Qu, Jiaxing;Stevanović, Vladan;Ertekin, Elif;Gorai, Prashun
• 通讯作者: Gorai, Prashun