DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining.

ABSTRACT: The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the Dig

SUBMITTER: Glasby LT 

PROVIDER: S-EPMC10269341 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature