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

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,
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.