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

第1部分：非技术总结本奖项是根据EAGER提案颁发的。它支持在利哈伊大学举行的SSMCDAT 2023数据马拉松上推进的项目进展。这个EAGER项目侧重于支持未来电池材料选择和设计的研究和教育活动。可充电电池中的电极材料在为设备供电和充电时必须可逆地吸收和释放锂离子和电子。材料的这种能力取决于它们所含原子的类型（组成）和原子的排列方式（原子结构）。为了更好地了解组成、原子结构和电池循环行为之间的关系，该项目组装了一个电池电极材料数据库。这包括编写程序，将原子结构信息从空间和视觉表示转换为数值，从而使这些信息与电池行为数据一起可视化。生成的数据库用于通过数据可视化以及使用回归和机器学习算法来识别原子结构、组成和功能之间的趋势和关系。数据科学算法的使用有助于在数据库中包含的数据类别之间建立非直观的高维相关性。通过这项工作创建的数据库和工具在开源许可下发布，并与文档和教程一起提供。此外，大学水平的课程材料是使用研究产品创建的，这有助于教授能量存储，数据科学以及与现实世界设备相关的原子结构和材料属性之间的关系。这个EAGER项目的重点是组装一个嵌入电池电极材料的数据库，该数据库将化学成分和循环行为与表示结构连通性的编码值相结合。为此，对现有资源进行了调整，并创建了新的程序，特别是将多面体内部和多面体之间的空间连接转换为数字表示。使用生成的数据库，通过可视化、回归和机器学习算法确定基本的结构-功能关系。从已建立的关系出发，根据材料的组成和结构选择具有目标循环行为的材料，通过实验制备并采用结构和电化学方法对其进行表征。实验结果为数据科学工具和结构-功能关系的修改和迭代应用提供了信息。通过该项目生成的数据库、工作流和程序在开源许可下发布，并与文档和教程一起提供。此外，高等教育课程的模块使用本研究的产品来推进结构-性质关系，电化学能量存储和数据科学工具的教学。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。

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.