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

EAGER：SSMCDAT2023：生成数据库以确定多面体间和多面体内连接和能量存储行为的趋势

• 批准号: 2334240
• 负责人: Megan Butala
• 金额: $ 20万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334240&HistoricalAwards=false
• 关键词: EAGER; SSMCDAT2023; Database; generation; identify

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.

第一部分： 非技术摘要本合同是根据EAGER的建议书签订的。它支持在利哈伊大学举行的SSMCDAT 2023数据会议上推进的项目进展。EAGER项目专注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必须在为设备供电和充电时可逆地吸收和释放锂离子和电子。材料做到这一点的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）。为了更好地理解成分、原子结构和电池循环行为之间的关系，本项目组装了一个电池电极材料数据库。这涉及到生成将原子结构信息从空间和视觉表示转换为数值的程序，这使得这些信息能够与电池行为数据一起可视化。生成的数据库用于通过数据的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和更高维的相关性。通过这项工作创建的数据库和工具在开源许可证下发布，并与文档和教程一起沿着。此外，大学水平的课程材料是使用研究产品创建的，这些产品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。 第二部分： 该EAGER项目专注于组装嵌入式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代表结构连接性的编码值相结合。为了做到这一点，现有的资源进行了调整，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识别基本的结构-功能关系。根据建立的关系，根据其组成和结构选择具有目标循环行为的材料，这些材料通过实验制备并采用结构和电化学方法进行表征。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证下发布，并与文档和教程沿着提供。此外，还利用该研究成果创建了高等教育课程模块，以推进结构-属性关系、电化学储能和数据科学工具的教学。该奖项反映了NSF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。