EAGER: SSMCDAT2023: Database generation to identify trends in inter- and intra-polyhedral connectivity and energy storage behavior

PART 1: NON-TECHNICAL SUMMARY This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This EAGER project focuses on research and education activities that support the selection and design of future battery materials. The electrode materials in a rechargeable battery must reversibly take in and release lithium-ions and electrons when powering a device and when charging. The ability of materials to do this depends on the types of atoms they contain (composition) and how the atoms are arranged (atomic structure). To better understand the relationships between composition, atomic structure, and battery cycling behavior, this project assembles a database of battery electrode materials. This involves producing programs that translate atomic structure information from spatial and visual representations into numerical values, which enables this information to be visualized alongside battery behavior data. The generated database is used to identify trends and relationships between atomic structure, composition, and function through visualization of data, as well as using regression and machine learning algorithms. The use of data science algorithms helps to establish unintuitive and higher dimensional correlations between data categories included in the database. The database and tools created through this work are published under an open-source license and made available along with documentation and tutorials. In addition, university-level course materials are created using research products, which help to teach about energy storage, data science, and relationships between atomic structure and materials properties relevant to real-world devices. PART 2: TECHNICAL SUMMARY This EAGER project focuses on assembling a database of intercalation battery electrode materials that combines chemical composition and cycling behavior with encoded values representing structural connectivity. To do so, existing resources are adapted and new programs are created, especially to translate spatial connectivity within and between polyhedra to numerical representations. Using the produced database, fundamental structure-function relationships are identified through visualization, regressions, and machine learning algorithms. From the established relationships, materials with targeted cycling behavior are selected based on their composition and structure, which are experimentally prepared and characterized with structural and electrochemical methods. Experimental results inform the modification and iterative application of data science tools and structure-function relationships. The database, workflows, and programs generated through this project are published under an open-source license and made available along with documentation and tutorials. In addition, modules for higher education courses are created using products of this research to advance instruction of structure-property relationships, electrochemical energy storage, and data science tools. 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.