CAREER: SusChEM: Data Mining to Reduce the Risk in Discovering New Sustainable Thermoelectric Materials
CAREER: SusChEM: Data Mining to Reduce the Risk in Discovering New Sustainable Thermoelectric Materials
批准号:
1651668
负责人:
Taylor Sparks
金额:
$58.0万
依托单位:
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
项目状态:
已结题
起止时间:
2017-07-01 至 2023-06-30
中文摘要
非技术性总结:人类在21世纪世纪面临着许多重大的工程挑战,从使太阳能负担得起,到发明新的科学发现工具,再到防止核恐怖等等。许多这些挑战的一个共同要求是需要发现新材料,但传统材料的发现是缓慢,低效和昂贵的。显然,需要一种新的工具来更快地开发新材料,并且成本很低。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的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。科学家们将能够使用这个工具来指导实验工作,以探索全新的化合物,否则风险太大。由于热电是可以将废热转换为电力的设备,因此该项目对美国的好处是巨大的。目前,近三分之二的能源作为废热损失,即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。学生将构建新型热电设备,并使用这些设备来执行双语西班牙语/英语推广到少数民族占多数的高中和初中学生在湖城。技术摘要:发现新材料是缓慢的,低效的,昂贵的。这些因素使得从化学白色空间中寻找新颖的新材料具有很高的风险。相反,大多数新的发展是在已知或已建立的结构类型,化学和系统中逐步发生的。然而,与探索化学白色空间的新化合物相关的风险可以通过利用材料信息学的新兴领域来减轻。在该提案中,将使用用于热电的材料推荐引擎来建议新颖的可持续热电组合物。该引擎仅使用组成来进行性能的概率估计,而不是计算昂贵的计算,这通常需要先验的晶体结构知识。避免晶体结构作为初始输入意味着可以使用此工具发现全新的化合物。发动机输出是组合物位于期望性能范围内的概率。因此,该项目将联合收割机将这些预测与现有的化合物应该形成的预测相结合,以实验探索新型热电材料。将通过纳入性能较差、中等以及良好的材料来改善用于算法和描述符开发的训练数据集,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。
英文摘要
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.
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DOI:
10.1007/s40192-020-00174-4
发表时间:
2020-06-01
期刊:
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
影响因子:
3.3
作者:
[Clement, Conrad L., Kauwe, Steven K., Sparks, Taylor D.]
通讯作者:
Sparks, Taylor D.
DOI:
10.1088/2515-7639/ac4ee5
发表时间:
2022-07-01
期刊:
JOURNAL OF PHYSICS-MATERIALS
影响因子:
4.8
作者:
[Titirici, Magda, Baird, Sterling G., Anderson, Paul A.]
通讯作者:
Anderson, Paul A.
DOI:
10.1107/s1600576722001492
发表时间:
2022
期刊:
Journal of Applied Crystallography
影响因子:
6.1
作者:
[Baird, Sterling G., Sparks, Taylor D.]
通讯作者:
Sparks, Taylor D.
Revised model for thermopower and site inversion in Co3O4 spinel
Co3O4 尖晶石热电和位点反转的修正模型
DOI:
10.1103/physrevb.98.024108
发表时间:
2018
期刊:
Physical Review B
影响因子:
3.7
作者:
[Sparks, Taylor D., Gurlo, Aleksander, Gaultois, Michael W., Clarke, David R.]
通讯作者:
Clarke, David R.
DOI:
10.1016/j.matt.2019.12.014
发表时间:
2020-02
期刊:
影响因子:
--
作者:
[Andrew R. Falkowski;Taylor D. Sparks]
通讯作者:
Andrew R. Falkowski;Taylor D. Sparks
共 32 条
EAGER: SSMCDAT2023: Natural Language Processing and Large Language Models for Automated Extraction of Materials Chemistry Data from Scientific Literature
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批准号:2334411
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项目类别:Standard Grant
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资助金额:$20.0万
-
财政年份:2023
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负责人:Taylor Sparks
-
依托单位:
REU Site: Research Experience in Utah for Sustainable Materials Engineering (ReUSE)
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批准号:1950589
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项目类别:Standard Grant
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资助金额:$37.29万
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财政年份:2020
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负责人:Taylor Sparks
-
依托单位:
Collaborative Research: SSMCDAT2020: Solid-State and Materials Chemistry Data Science Hackathon
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批准号:1938734
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项目类别:Standard Grant
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资助金额:$6.26万
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财政年份:2019
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负责人:Taylor Sparks
-
依托单位:
Collaborative Research: Guided Discovery of Sustainable Superhard Materials via Bond Optimization
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批准号:1562226
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项目类别:Standard Grant
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资助金额:$25.0万
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财政年份:2016
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负责人:Taylor Sparks
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依托单位:
海外基金