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.
