CAREER: SusChEM: Data Mining to Reduce the Risk in Discovering New Sustainable Thermoelectric Materials

职业：SusChEM：通过数据挖掘降低发现新型可持续热电材料的风险

基本信息

批准号：
1651668
负责人：
Taylor Sparks
金额：
$ 58万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651668&HistoricalAwards=false
关键词：
CAREER SusChEM Data Mining Reduce

项目摘要

NON-TECHNICAL SUMMARY:Humanity faces a number of grand challenges in engineering in the 21st century ranging from making solar energy affordable, to inventing new tools for scientific discovery, to preventing nuclear terror and more. A common requirement to many of these challenges is the need to discover new materials but traditional materials discovery is slow, inefficient, and expensive. Clearly, a new tool is required to develop new materials faster and at a fraction of the cost. One possibility is to rely on big data to accelerate materials discovery. This project serves the national interest by using data mining tools to create a materials recommendation engine for new sustainable thermoelectric materials. This engine will provide recommendations for new materials based off of statistical probability of desired performance. Scientists will be able to use this tool to guide experimental efforts to explore totally new compounds that would be too risky to investigate otherwise. Since thermoelectrics are devices that can convert waste heat to electricity the potential for this project to benefit the United States is significant. Currently close to two thirds of energy is lost as waste heat and recovering even a small fraction of this with new thermoelectric materials would amount to enormous energy savings. The PI will also leverage this research opportunity to supplement his teaching and outreach efforts. Students will construct novel thermoelectric devices and use these devices to perform bilingual Spanish/English outreach to minority-majority high school and junior high students in Salt Lake City.TECHNICAL SUMMARY:Discovering new materials is slow, inefficient, and expensive. These factors make searching for novel new materials from chemical white space very high risk. Instead, most new developments occur incrementally in already known or established structure types, chemistries, and systems. However, the risk associated with exploring chemical white space for new compounds can be mitigated by utilizing the emergent field of materials informatics. In this proposal novel, sustainable thermoelectric compositions will be suggested using a materials recommendation engine for thermoelectrics. The engine uses composition only to make probabilistic estimates of performance rather than computationally expensive calculations which generally require knowledge of the crystal structure a priori. Avoiding crystal structure as an initial input means entirely new compounds can be discovered with this tool. The engine output is a probability of compositions lying within a desired performance range. Therefore, this project will combine these predictions with existing predictions of where compounds should form to experimentally explore novel thermoelectric materials. The training data set for algorithm and descriptor development will be improved by inclusion of performance of poor, mediocre, as well as good materials to overcome the bias in literature for high-performing materials. Experimental synthesis and characterization will be carried out on suggested compounds and for algorithm validation.

非技术概要：人类在 21 世纪面临着工程方面的许多重大挑战，从使太阳能变得负担得起，到发明用于科学发现的新工具，到防止核恐怖等等。许多这些挑战的共同要求是需要发现新材料，但传统材料的发现速度慢、效率低且昂贵。显然，需要一种新工具来更快地以较低的成本开发新材料。一种可能性是依靠大数据来加速材料发现。该项目通过使用数据挖掘工具为新型可持续热电材料创建材料推荐引擎来服务于国家利益。该引擎将根据所需性能的统计概率为新材料提供建议。科学家将能够使用这个工具来指导探索全新化合物的实验工作，否则进行研究的风险太大。由于热电装置可以将废热转化为电能，因此该项目为美国造福的潜力是巨大的。目前，近三分之二的能量以废热的形式损失掉，即使使用新的热电材料回收其中的一小部分，也将节省大量能源。 PI 还将利用这一研究机会来补充他的教学和推广工作。学生将构建新颖的热电设备，并使用这些设备对盐湖城的少数族裔高中生和初中生进行西班牙语/英语双语推广。技术摘要：发现新材料缓慢、低效且昂贵。这些因素使得从化学空白区寻找新颖的新材料的风险非常高。相反，大多数新的发展是在已知或已建立的结构类型、化学物质和系统中逐步发生的。然而，通过利用新兴的材料信息学领域可以减轻与探索新化合物的化学空白相关的风险。在本提案中，将使用热电材料推荐引擎来建议新颖的、可持续的热电组合物。该引擎仅使用成分来对性能进行概率估计，而不是进行昂贵的计算，而计算通常需要先验了解晶体结构。避免将晶体结构作为初始输入意味着可以使用该工具发现全新的化合物。发动机输出是成分处于期望性能范围内的概率。因此，该项目将把这些预测与化合物应在何处形成的现有预测结合起来，以实验探索新型热电材料。用于算法和描述符开发的训练数据集将通过纳入性能较差、平庸以及良好的材料来改进，以克服文献中对高性能材料的偏见。将对建议的化合物进行实验合成和表征并进行算法验证。

项目成果

期刊论文数量（44）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Data-Driven Studies of Li-Ion-Battery Materials

锂离子电池材料的数据驱动研究

DOI：
10.3390/cryst9010054
发表时间：
2019-01-18
期刊：
Crystals
影响因子：
2.7
作者：
Steven K. Kauwe;T. Rhone;Taylor D. Sparks
通讯作者：
Taylor D. Sparks

What is a minimal working example for a self-driving laboratory?

自动驾驶实验室的最小工作示例是什么？

DOI：
10.1016/j.matt.2022.11.007
发表时间：
2022-12-01
期刊：
Matter
影响因子：
18.9
作者：
Sterling G. Baird;Taylor D. Sparks
通讯作者：
Taylor D. Sparks

Not Just Par for the Course: 73 Quaternary Germanides RE 4 M 2 X Ge 4 ( RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity

不仅仅是课程的标准杆：73 四元锗化物 RE 4 M 2 X Ge 4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) 和搜索

DOI：
10.1021/acs.inorgchem.8b02279
发表时间：
2018-11
期刊：
Inorganic Chemistry
影响因子：
4.6
作者：
Zhang, Dong;Oliynyk, Anton O.;Duarte, Gabriel M.;Iyer, Abishek K.;Ghadbeigi, Leila;Kauwe, Steven K.;Sparks, Taylor D.;Mar, Arthur
通讯作者：
Mar, Arthur

Not as simple as we thought: a rigorous examination of data aggregation in materials informatics

并不像我们想象的那么简单：材料信息学中数据聚合的严格检查