Browsing by Author "Gong, Z"

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    EMFDTW: an automated crystallographic identification tool supporting multiple comparison criteria
    (American Chemical Society, 2024-07-13) He, B; Meng, Y; Gong, Z; Wang, K; Jiang, Z; Avdeev, M; Shi, SQ
    Identification of the same and similar crystal structures assists in searching for duplicate materials data and discovering prototype structures. Although several structure identification methods exist, their requirements for the input information limit their ability to accurately and automatically process structures within big materials databases and especially distinguish disordered ion conductor structures due to the site occupancy uncertainty of migration ions. Here, we introduce an automated crystal structure identification method called EMFDTW, in which a set of eigen-subspace modular functions (EMFs) is derived from a distance matrix incorporating site type identifiers, and then the similarity between them is measured through dynamic time warping (DTW). In this way, not only the conventional spatial sites in the crystal structure but also the atomic attributes (type, occupancy, oxidation state, magnetic moment, etc.) on the sites can be considered as the comparative features. Furthermore, by conducting a skeleton similarity analysis on 113,586 crystal structures sourced from the crystallography open database and the inorganic crystal structure database, we establish a database of 17,340 skeleton prototypes, which paves the way for searching potential ionic conductors. Our work provides an easy-to-use tool to analyze complex crystal structures, providing new insights for the discovery and design of new materials. © 2024 American Chemical Society.
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    FFMDFPA: a FAIRification framework for materials data with no-code flexible semi-structured parser and application programming interfaces
    (American Chemical Society, 2023-08-28) He, B; Gong, Z; Avdeev, M; Shi, S
    The FAIR Data Principles are guidelines to ensure Findability, Accessibility, Interoperability, and Reusability of digital resources, which are essential to accelerate data-driven materials science. Despite the development and growing adoption of the FAIR principles, appropriate implementation solutions and software to make data FAIR are still sparse, particularly in standardization of heterogeneous data and subsequent data access. Here, we introduce a FAIRification Framework for Materials Data with No-Code Flexible Semi-Structured Parser and API (FFMDFPA) (API, application programming interface) for raw data processing. Using a template-based parser, FFMDFPA can extract and transform semistructured data in various text formats, providing the flexibility to extend data manipulation without coding. Additionally, FFMDFPA provides a standardized API with efficient query syntax that facilitates seamless data sharing. Taking various text files generated by computational software as examples, we demonstrate the potential utility of FFMDFPA. This work offers important insights toward efficient utilization and reuse of materials data, and the data semantic manipulation implemented in the parser and API can be extended to textual data, which has implications for future data FAIRification. © American Chemical Society