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

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差，并且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对先进热电材料的研究。这些努力建立在一些团队成员先前关于预测材料热电性能潜力的NSF DMREF研究的基础上。高通量筛选集中于鉴定具有所需电子和振动性质的半导体。 然而，这些努力并不包括对内在缺陷的作用或可掺杂性的潜力的强烈关注。 在这项研究的下一阶段，这些关键组件将通过高通量理论，实验验证和机器学习的混合来实现。 总之，这些努力将产生对数千种半导体的热电势的准确预测，并实现用于固态发电的新材料。 除了热电材料之外，建立掺杂推荐引擎的这些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剂）的开发至关重要。 技术说明：该项目的最终目标是建立一个强大而准确的掺杂推荐引擎，以克服热电材料发现中的掺杂瓶颈。推荐引擎将使用材料信息学来实现掺杂性的高通量预测，仅依赖于计算成本低的量、实验测量和已知的结构/化学特征作为输入。因此，它将允许数千种化合物的掺杂性筛选。 首先，将为推荐引擎构建准确的训练集，该训练集包含来自各种热电相关化合物的天然缺陷形成特征和结构/化学描述符。鉴于之前的掺杂剂研究集中在单一化合物上，将利用一种新的自动计算基础设施，快速创建一个广泛的训练集，最初包含大约30种化合物，但在项目期间将增加到100多种。实验电荷传输和局部掺杂剂结构测量将验证训练集。其次，预测引擎将在数据上进行训练，以提取模式和相关性，并最终识别可掺杂性的鲁棒描述符。 最初，引擎将预测“杀手”缺陷是否限制了可用的掺杂剂范围。该引擎最终将发展为通过初始筛选的化合物的特定外在掺杂剂。 总之，这种对固有输运性质（先前的DMREF）和掺杂性（拟议的DMREF）的准确预测的组合有望加速热电材料的发现过程。

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.