Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time consuming, Edisonian processes. Information gleaned from scientific literature can help inform and accelerate nanomaterials development. However, searching the literature and digesting the information are time consuming processes for researchers.
To help address these challenges, we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledge base for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of over thirty-five thousand nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ texts, and extract and normalize chemical terms within synthesis protocols. By extracting the synthesis protocols from these articles and then extracting the chemicals used in the synthesis, we showed that further correlations can be identified between the use of certain reagents and specific nanomaterial morphologies and compositions. In addition to processing texts, the tools automatically identified and analyzed microscopy images of nanomaterials within articles to determine nanomaterial morphologies and size distributions. To enable users to easily explore the database, we developed a complementary, browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We used these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which was useful in guiding hypotheses and reducing the potential parameter space during experimental design.
This research supports the Department of Energy's goal to advance fundamental science and technology innovation, and it enhances the Laboratory's core competencies in materials, high-performance computing, computer simulation, and data science. By itself, the classification of articles can provide insights as to what combination of nanomaterial composition and morphology are over- or under-explored compared to the average, thereby potentially indicating hot topic areas.
Han,T. 2016. "Multiphase Separation of Copper Nanowire." Chemical Communications 52:11627–11630. doi:10.1039/c6cc06228h. LLNL-JRNL-698864.
——— . 2017. "Tunable Amorphous Photonic Materials with Pigmentary Colloidal Nanostructure." Advanced Optical Materials 5 (7):1600838. doi:10.1002/adom.201600838. LLNL-JRNL-715397.
——— . 2017. "Ultralight Conductive Silver Nanowire Aerogels." Nano Letters 17(12): 7171–7176. doi: 10.1021/acs.nanolett.7b02790. LLNL-JRNL-730883.
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