ArticleJournal of chemical information and modeling2025
Cost-Efficient Domain-Adaptive Pretraining of Language Models for Optoelectronics Applications.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Autogenerating a Domain-Specific Question-Answering Data Set to Enable High-Performing Language Models for Magnetic Materials.Journal of chemical information and modeling · 2026Article
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Machine-Learning Predictions of Photoluminescence in Molecules Exhibiting Thermally Activated Delayed Fluorescence with Implicit Experimental Validation.Journal of chemical information and modeling · 2026Article
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2 authors.
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Abstract
Pretrained language models have demonstrated strong capability and versatility in natural language processing (NLP) tasks, and they have important applications in optoelectronics research, such as data mining and topic modeling. Many language models have also been developed for other scientific domains, among which Bidirectional Encoder Representations from Transformers (BERT) is one of the most widely used architectures. We present three "optoelectronics-aware" BERT models, OE-BERT, OE-ALBERT, and OE-RoBERTa, that outperform both their counterpart general English models and larger models in a variety of NLP tasks about optoelectronics. Our work also demonstrates the efficacy of a cost-effective domain-adaptive pretraining (DAPT) method with RoBERTa, which significantly reduces computational resource requirements by more than 80% for its pretraining while maintaining or enhancing its performance. All models and data sets are available to the optoelectronics-research 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.