ArticleJournal of chemical information and modeling2022
BatteryBERT: A Pretrained Language Model for Battery Database Enhancement.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 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
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- LLMB: AI Agent for Lithium Metal Battery Research Using Large Language Model.ACS central science · 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
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Advancing battery research through large language models: A review.Innovation (Cambridge (Mass.)) · 2026Review
- Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries.Science advances · 2025Review
- 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
- Tracking 35 years of progress in metallic materials for extreme environments via text mining.Scientific reports · 2025Article
- MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.Journal of chemical information and modeling · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- Potential of Progressive and Disruptive Innovation-Driven Cost Reductions of Green Hydrogen Production.Energy & fuels : an American Chemical Society journal · 2024Article
- Creation of a structured solar cell material dataset and performance prediction using large language models.Patterns (New York, N.Y.) · 2024Article
- How Beneficial Is Pretraining on a Narrow Domain-Specific Corpus for Information Extraction about Photocatalytic Water Splitting?Journal of chemical information and modeling · 2024Article
- A document-level information extraction pipeline for layered cathode materials for sodium-ion batteries.Scientific data · 2024Article
- BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
2 authors.
Funding
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Abstract
A great number of scientific papers are published every year in the field of battery research, which forms a huge textual data source. However, it is difficult to explore and retrieve useful information efficiently from these large unstructured sets of text. The Bidirectional Encoder Representations from Transformers (BERT) model, trained on a large data set in an unsupervised way, provides a route to process the scientific text automatically with minimal human effort. To this end, we realized six battery-related BERT models, namely, BatteryBERT, BatteryOnlyBERT, and BatterySciBERT, each of which consists of both cased and uncased models. They have been trained specifically on a corpus of battery research papers. The pretrained BatteryBERT models were then fine-tuned on downstream tasks, including battery paper classification and extractive question-answering for battery device component classification that distinguishes anode, cathode, and electrolyte materials. Our BatteryBERT models were found to outperform the original BERT models on the specific battery tasks. The fine-tuned BatteryBERT was then used to perform battery database enhancement. We also provide a website application for its interactive use and visualization.
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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.