Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge Article Swipe
Anna M. Hiszpanski
,
Brian Gallagher
,
Karthik Chellappan
,
Peggy Li
,
Shusen Liu
,
Hyojin Kim
,
Jinkyu Han
,
Bhavya Kailkhura
,
David Buttler
,
T. Yong-Jin Han
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.0c00199
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.0c00199
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.jcim.0c00199
- OA Status
- green
- Cited By
- 59
- References
- 78
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W3015467311Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1021/acs.jcim.0c00199Digital Object Identifier
- Title
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Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing KnowledgeWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2020Year of publication
- Publication date
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2020-04-14Full publication date if available
- Authors
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Anna M. Hiszpanski, Brian Gallagher, Karthik Chellappan, Peggy Li, Shusen Liu, Hyojin Kim, Jinkyu Han, Bhavya Kailkhura, David Buttler, T. Yong-Jin HanList of authors in order
- Landing page
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https://doi.org/10.1021/acs.jcim.0c00199Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/1669214Direct OA link when available
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Computer science, Categorization, Visualization, Nanomaterials, Flexibility (engineering), Identification (biology), Structuring, Data science, Nanotechnology, Artificial intelligence, Materials science, Biology, Statistics, Mathematics, Finance, Economics, BotanyTop concepts (fields/topics) attached by OpenAlex
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59Total citation count in OpenAlex
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2025: 10, 2024: 9, 2023: 16, 2022: 9, 2021: 13Per-year citation counts (last 5 years)
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78Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I1282311441 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.93214946 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |