ArticleiScience2021
Automated knowledge extraction from polymer literature using natural language processing.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Enhanced global oil spill dataset from 1967 to 2023 based on text-form incident information.Scientific data · 2025Article
- Domain-specific embeddings uncover latent genetics knowledge.bioRxiv : the preprint server for biology · 2025Article
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers.Polymer chemistry · 2025Review
- AI-Based Knowledge Extraction from the Bioprinting Literature for Identifying Technology Trends.3D printing and additive manufacturing · 2024Article
- Emerging Trends in Machine Learning: A Polymer Perspective.ACS polymers Au · 2023Review
- A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing.npj computational materials · 2023Article
- BatteryBERT: A Pretrained Language Model for Battery Database Enhancement.Journal of chemical information and modeling · 2022Article
- Leveraging Theory for Enhanced Machine Learning.ACS macro letters · 2022Article
- UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites.Genes · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Materials science literature has grown exponentially in recent years making it difficult for individuals to master all of this information. This constrains the formulation of new hypotheses that scientists can come up with. In this work, we explore whether materials science knowledge can be automatically inferred from textual information contained in journal papers. Using a data set of 0.5 million polymer papers, we show, using natural language processing methods that vector representations trained for every word in our corpus can indeed capture this knowledge in a completely unsupervised manner. We perform time-based studies through which we track popularity of various polymers for different applications and predict new polymers for novel applications based solely on the domain knowledge contained in our data set. Using co-relations detected automatically from literature in this manner thus, opens up a new paradigm for materials discovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.