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

从便携式电子设备和传感器的部署到电动汽车和混合动力汽车，以及国家电网的可靠性和安全性，迫切需要加快开发和部署可扩展、可靠、安全和与位置无关的能量存储的能力。以数据为基础的发现和设计转变液体储能(D3TaLES)研究基础设施改进Track-2专注于EPSCoR协作(RII Track-2 FEC)的使命是创造材料科学的新领域知识。该项目还将利用大规模创建、收集、分析和建模精选数据，为满足这些技术挑战的下一代电池设计材料。我们的工作得到了跨学科和多样化的合作伙伴网络的支持，这些合作伙伴在材料设计、表征和部署、自主实验、数据分析和机器学习以及项目评估和评估方面拥有专业知识。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以开发学科知情模型，以及促进职业早期教师的发展来领导大型研究中心，从而扩大对科学和工程的参与。该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职员工。由于所涉及的化学和物理空间的高维性，液基储能材料的合理设计是一个相当大的挑战。为了应对这一问题，基于数据的发现和设计转变液体储能(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)创建经过审查的、已发表的LES特征数据库和自动化实验，以便更有效地生成LES性质数据；(Ii)评估氧化还原活性分子的溶液行为和溶剂化环境随其分子结构和电荷状态的变化；(Iii)将高浓度下分析物的溶解度和溶剂化与影响传统和离子液体(IL)中的溶剂和分析物结构以及相变的基本化学相互作用联系起来；以及(Iv)开发和部署稳健、直观的ML模型，以预测化学上新颖的LES材料。D3TaLES还提供了广泛培训和劳动力发展的机会，这将产生广泛的影响。合作领导力学院(ACL)将培养职业早期教师，以建立和领导不同的研究人员群体，并担任下一代学术研究中心主任。项目人员将接受超越其核心学科的培训，将化学和数据科学、ML和自主实验之间的专业知识联系起来。为本科生提供的将基础研究与社会背景联系起来的培训机会将旨在增加对STEM工作的参与和保留。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.