Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Hiszpanski, Anna M.; Gallagher, Brian; Chellappan, Karthik; Li, Peggy; Liu, Shusen; Kim, Hyojin; Han, Jinkyu; Kailkhura, Bhavya; Buttler, David J.; Han, Thomas Yong-Jin

doi:10.1021/acs.jcim.0c00199

Title: Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Journal Article · Tue Apr 14 00:00:00 EDT 2020 · Journal of Chemical Information and Modeling

DOI:https://doi.org/10.1021/acs.jcim.0c00199· OSTI ID:1669214

Hiszpanski, Anna M. ^[1]; Gallagher, Brian ^[2]; Chellappan, Karthik ^[3]; Li, Peggy ^[3]; Liu, Shusen ^[2]; Kim, Hyojin ^[2];

^[1]; Kailkhura, Bhavya ^[2]; Buttler, David J. ^[2];

^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Materials Science Division
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Global Security Computing Applications Division

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 and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, here 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 knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ~35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials’ 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 use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

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Research Organization:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1669214

Report Number(s):: LLNL-JRNL-779959; 971534

Journal Information:: Journal of Chemical Information and Modeling, Vol. 60, Issue 6; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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