RII Track-2 FEC: Data-Enabled Discovery and Design to Transform Liquid-Based Energy Storage (D3TaLES)

RII Track-2 FEC：基于数据的发现和设计，以改变液体能源存储 (D3TaLES)

• 批准号: 2019574
• 负责人: Chad Risko
• 金额: $ 397.95万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019574&HistoricalAwards=false
• 关键词: RII; Track; FEC; Data; Enabled

From the deployment of portable electronics and sensors to electric and hybrid vehicles and the reliability and security of the nation's electrical grid, there is tremendous need to accelerate the ability to develop and deploy scalable, reliable, safe, and location-independent energy storage. The mission of the Data-enabled Discovery and Design to Transform Liquid-based Energy Storage(D3TaLES) Research Infrastructure Improvement Track-2 Focused EPSCoR Collaboration (RII Track-2 FEC) is to create new domain knowledge in materials science. This project will also capitalize on the large-scale creation, collection, analysis, and modeling of curated data to design materials for next-generation batteries that meet these technological challenges. Our work is enabled by an interdisciplinary and diverse network of collaborators with expertise in materials design, characterization, and deployment, autonomous experimentation, data analytics, and machine learning, and program evaluation and assessment. D3TaLES will broaden participation in science and engineering by recruiting and training students from predominantly rural communities to be leading energy scientists, cross-training physical scientists and data scientists to enable the development of discipline-informed models, and fostering the development of early-career faculty to lead large-scale research centers. This team includes faculty, students, and staff from the University of Kentucky, Eastern Kentucky University, University of Iowa, Iowa State University, Cornell College, and the University of Northern Iowa. The rational design of liquid-based energy-storage (LES) materials is a considerable challenge due to the high dimensionality of the chemical and physical spaces involved. To confront this problem, Data-enabled Discovery and Design to Transform Liquid-based Energy Storage(D3TaLES) establishes an interdisciplinary, collaborative team that combines domain knowledge in materials creation and characterization with emerging best practices in data science and machine learning (ML) to advance LES discovery and design paradigms. The four technical objectives of D3TaLES are to (i) create a database of vetted, published LES characteristics and automate experimentation for more efficient generation of LES property data, (ii) evaluate changes in the solution behavior and solvation environment of redox-active molecules as a function of their molecular structure and charge state, (iii) link analyte solubility and solvation at high concentrations to fundamental chemical interactions as it impacts solvent and analyte structures and phase transitions in traditional and ionic liquids (IL), and (iv) develop and deploy robust, intuitive ML models to predict chemically novel LES materials. D3TaLES also presents opportunities for extensive training and workforce development that will have wide-ranging impact. The Academy for Collaborative Leadership (ACL) will prepare early-career faculty to build and lead diverse groups of researchers and serve as the next-generation of academic research-center directors. Project personnel will be trained beyond their core disciplines, bridging expertise between chemistry and data sciences, ML, and autonomous experimentation. Training opportunities for undergraduate students that connect fundamental research with social context will be designed to increase participation and retention in the STEM workforce.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

从便携式电子设备和传感器的部署到电动和混合动力汽车，再到国家电网的可靠性和安全性，我们迫切需要加快开发和部署可扩展、可靠、安全、与位置无关的能源存储的能力。基于数据的发现和设计改造基于液体的能源存储（D3TaLES）研究基础设施改进轨道2重点EPSCoR协作（RII Track-2 FEC）的使命是在材料科学领域创造新的领域知识。该项目还将利用大规模的创建、收集、分析和建模，为下一代电池设计材料，以应对这些技术挑战。我们的工作是由跨学科和多样化的合作者网络实现的，他们在材料设计，表征和部署，自主实验，数据分析，机器学习以及项目评估和评估方面具有专业知识。D3TaLES将通过招募和培训主要来自农村社区的学生，使他们成为领先的能源科学家，交叉培训物理科学家和数据科学家，使学科知识模型的发展成为可能，并促进早期职业教师的发展，以领导大型研究中心，从而扩大科学和工程领域的参与。这个团队包括来自肯塔基大学、东肯塔基大学、爱荷华大学、爱荷华州立大学、康奈尔学院和北爱荷华大学的教师、学生和工作人员。由于所涉及的化学和物理空间的高维性，液体储能材料的合理设计是一个相当大的挑战。为了解决这个问题，D3TaLES建立了一个跨学科的协作团队，将材料创造和表征方面的领域知识与数据科学和机器学习（ML）的新兴最佳实践相结合，以推进LES发现和设计范式。D3TaLES的四个技术目标是：(i)创建一个经过审查和发布的LES特性数据库，并自动实验，以更有效地生成LES特性数据；（ii）评估氧化还原活性分子的溶液行为和溶剂化环境的变化，作为其分子结构和电荷状态的函数。（iii）将分析物的溶解度和高浓度溶剂化与基本化学相互作用联系起来，因为它影响传统液体和离子液体（IL）中的溶剂和分析物的结构和相变；（iv）开发和部署强大、直观的ML模型，以预测化学上新颖的LES材料。D3TaLES还提供了广泛的培训和劳动力发展机会，将产生广泛的影响。合作领导学院（ACL）将培养早期职业教师，以建立和领导不同的研究小组，并担任下一代学术研究中心主任。项目人员将接受核心学科以外的培训，在化学与数据科学、机器学习和自主实验之间架起桥梁。为将基础研究与社会背景联系起来的本科生提供培训机会，旨在提高STEM劳动力的参与度和保留率。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Challenges in Information-Mining the Materials Literature: A Case Study and Perspective

材料文献信息挖掘的挑战：案例研究和视角

• DOI: 10.1021/acs.chemmater.2c00445
• 发表时间: 2022
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: Smith, Andrew;Bhat, Vinayak;Ai, Qianxiang;Risko, Chad
• 通讯作者: Risko, Chad

Large Variability and Complexity of Isothermal Solubility for a Series of Redox-Active Phenothiazines

• DOI: 10.1039/d2ma00598k
• 发表时间: 2022
• 期刊: Materials Advances
• 影响因子: 5
• 作者: A. Perera;T. M. Suduwella;N. Attanayake;Rahul Kant Jha;William L. Eubanks;I. Shkrob;C. Risko;A. Kaur;S. Odom

Probing Redox Properties of Extreme Concentrations Relevant for Nonaqueous Redox-Flow Batteries

• DOI: 10.1021/acsaem.2c03712
• 发表时间: 2023-03
• 期刊: ACS Applied Energy Materials
• 影响因子: 6.4
• 作者: Nathan C Stumme;A. Perera;A. Horvath;Sashen Ruhunage;Darby H. Duffy;Elise M. Koltonowski;Jackson Tupper;Chad Dzierba;Alie D. McEndaffer;Craig M. Teague;C. Risko;S. Shaw