ArticlePatterns (New York, N.Y.)2022
Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Sustainable Polymer Aerogels: Multiscale Design from Biomass and Thermoset Networks to AI-Guided Materials Discovery.Gels (Basel, Switzerland) · 2026Review
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- DA-BioNER: data augmentation based on few-shot learning and distant supervision for biomedical named entity recognition.Bioinformatics (Oxford, England) · 2026Article
- Enhancing composition-based materials property prediction by cross-modal knowledge transfer.Scientific reports · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Navigating Ternary Doping in Li-ion Cathodes With Closed-Loop Multi-Objective Bayesian Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Generative Models for Crystalline Materials.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model.Scientific data · 2025Article
- Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models.Journal of chemical information and modeling · 2025Article
- Ontology-conformal recognition of materials entities using language models.Scientific reports · 2025Article
- Exploration of crystal chemical space using text-guided generative artificial intelligence.Nature communications · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- AI Thinking: a framework for rethinking artificial intelligence in practice.Royal Society open science · 2025Article
- Creation of a structured solar cell material dataset and performance prediction using large language models.Patterns (New York, N.Y.) · 2024Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Structured information extraction from scientific text with large language models.Nature communications · 2024Article
- Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.PloS one · 2024Article
- Accurate, interpretable predictions of materials properties within transformer language models.Patterns (New York, N.Y.) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A bottleneck in efficiently connecting new materials discoveries to established literature has arisen due to an increase in publications. This problem may be addressed by using named entity recognition (NER) to extract structured summary-level data from unstructured materials science text. We compare the performance of four NER models on three materials science datasets. The four models include a bidirectional long short-term memory (BiLSTM) and three transformer models (BERT, SciBERT, and MatBERT) with increasing degrees of domain-specific materials science pre-training. MatBERT improves over the other two BERT
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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.