ArticleJournal of chemical information and modeling2023
OpticalBERT and OpticalTable-SQA: Text- and Table-Based Language Models for the Optical-Materials Domain.
Article in Journal of chemical information and modeling, 2023. 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.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Article
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- Automatic Generation of a Mechanical Properties Question-Answering Data Set for Language Model Benchmarking: A Comparative Study of BERT, XLNet, and LLaMA Models.Journal of chemical information and modeling · 2026Article
- Prompt Engineering Accelerates the Data-Driven Discovery of Photocatalysts via an LLM-Based Model Ensemble Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SciDaSynth: Interactive Structured Data Extraction From Scientific Literature With Large Language Model.Campbell systematic reviews · 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
- Cost-Efficient Domain-Adaptive Pretraining of Language Models for Optoelectronics Applications.Journal of chemical information and modeling · 2025Article
- MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.Journal of chemical information and modeling · 2025Article
- How Beneficial Is Pretraining on a Narrow Domain-Specific Corpus for Information Extraction about Photocatalytic Water Splitting?Journal of chemical information and modeling · 2024Article
- BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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3 authors.
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Abstract
Text mining in the optical-materials domain is becoming increasingly important as the number of scientific publications in this area grows rapidly. Language models such as Bidirectional Encoder Representations from Transformers (BERT) have opened up a new era and brought a significant boost to state-of-the-art natural-language-processing (NLP) tasks. In this paper, we present two "materials-aware" text-based language models for optical research, OpticalBERT and OpticalPureBERT, which are trained on a large corpus of scientific literature in the optical-materials domain. These two models outperform BERT and previous state-of-the-art models in a variety of text-mining tasks about optical materials. We also release the first "materials-aware" table-based language model, OpticalTable-SQA. This is a querying facility that solicits answers to questions about optical materials using tabular information that pertains to this scientific domain. The OpticalTable-SQA model was realized by fine-tuning the Tapas-SQA model using a manually annotated OpticalTableQA data set which was curated specifically for this work. While preserving its sequential question-answering performance on general tables, the OpticalTable-SQA model significantly outperforms Tapas-SQA on optical-materials-related tables. All models and data sets are available to the optical-materials-science community.
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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.