Abstract
In recent years, metal-polyphenol networks (MPNs) have gained significant attention due to their unique properties and broad applications across various fields. However, the burgeoning volume of MPN literature necessitates the automation of chemical information extraction from the extensive corpus of unstructured data, including scientific publications. To address this challenge, we proposed a platform named MPNTEXT, which utilized natural language processing techniques and machine learning algorithms to efficiently identify and extract pertinent information, thereby assisting users in comprehending complex MPNs and their textual descriptions of applications. Users can enter keywords, such as "Fe", "drug delivery", or "tannic acid", to retrieve relevant information, which is then presented in a structured format. This study aims to provide a user-friendly tool for collecting and retrieving MPN data and promotes data-driven material design. The platform offers researchers a more convenient and efficient way to design versatile MPNs and explore their applications.
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