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An automatic scientific data collection framework for materials science
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AI
DOI：10.1016/j.commatsci.2025.113772.png)
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Abstract
En 中文
With the rapid development of information technology, there has been an exponential increase in material data. However, challenges such as inconsistencies in data formats and non-standardized storage methods have emerged as primary obstacles for researchers seeking to harness materials science data effectively. To fully exploit material data from diverse sources and achieve the efficient fusion of historical data, this paper introduces a database application framework designed for the automatic collection and analysis of multi- source heterogeneous material data, and two first principles calculations datasets are established. Standardized methods used in this work enable the automatic extraction, storage and analysis of both discrete and database data while also offering an interface for data-driven scientific research. Moreover, this framework used for dataset construction can be deployed in both cloud-based virtual environments and local servers, providing flexibility that not only facilitates data sharing but also ensures data privacy and customized control. The datasets and framework developed in this work offer a robust data foundation and potent tool for researchers engaged in data-driven research.
Keywords:
Material
Database
Collection framework
Scientific computing
Journal
IF:
3.3
Papers:
1.3W
Citations:
3.6W