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Related Concept Videos

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The naming of enantiomers employs the Cahn–Ingold–Prelog rules that involve assigning priorities to different substituent groups at a chiral center. Each enantiomer, being a distinct molecule, is assigned a unique name by the Cahn–Ingold–Prelog (CIP) rules, also called the R–S system. The prefix R- or S- attached to the chiral centers in an enantiomer is dependent on the spatial arrangement of the four substituents on the chiral center. The R–S system essentially comprises three...
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Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
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Related Experiment Video

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Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese
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Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science

L Weston, V Tshitoyan, J Dagdelen

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    We developed a text mining approach using named entity recognition (NER) to extract key information from millions of materials science articles, creating a structured database to accelerate materials discovery.

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    Area of Science:

    • Materials Science
    • Computational Materials Science
    • Data Science

    Background:

    • The rapid growth of materials science literature presents a challenge in synthesizing existing knowledge.
    • Connecting new research findings with established literature is a bottleneck in materials discovery.

    Purpose of the Study:

    • To develop a text mining framework for large-scale information extraction from materials science literature.
    • To create a structured database of materials science knowledge for programmatic querying.

    Main Methods:

    • Application of text mining with named entity recognition (NER) for information extraction.
    • Training an NER model to identify inorganic materials, sample descriptors, properties, synthesis, and characterization methods.
    • Processing 3.27 million materials science abstracts to extract over 80 million named entities.

    Main Results:

    • Achieved 87% accuracy (f1-score) in extracting summary-level information.
    • Converted millions of abstracts into structured database entries.
    • Demonstrated the ability to answer complex literature-based questions via simple database queries.

    Conclusions:

    • The developed system effectively extracts and structures information from materials science literature.
    • This approach accelerates materials discovery by enabling efficient querying of vast amounts of data.
    • The data and tools are publicly available to foster further research and development.