Cover Image

Large language model-enabled automated data extraction for concrete materials informatics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Zhanzhao, Yang, Kengran, He, Qiyao, Gong, Kai
Format:	Preprint
Published:	2026
Subjects:	Materials Science Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics
by: Moran, Michael, et al.
Published: (2024)

Large language models in materials science and the need for open-source approaches
by: Yang, Fengxu, et al.
Published: (2025)

Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models
by: Katzer, Balduin, et al.
Published: (2025)

Visualizing token importance for black-box language models
by: Rauba, Paulius, et al.
Published: (2025)

Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials
by: Xu, HuangChao, et al.
Published: (2024)

Quantifying perturbation impacts for large language models
by: Rauba, Paulius, et al.
Published: (2024)

Probing the limitations of multimodal language models for chemistry and materials research
by: Alampara, Nawaf, et al.
Published: (2024)

Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing
by: Shetty, Pranav, et al.
Published: (2024)

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
by: Zhan, Hualin, et al.
Published: (2024)

Text-to-Battery Recipe: A language modeling-based protocol for automatic battery recipe extraction and retrieval
by: Lee, Daeun, et al.
Published: (2024)

Large language model as user daily behavior data generator: balancing population diversity and individual personality
by: Li, Haoxin, et al.
Published: (2025)

Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery
by: Jia, Yunze, et al.
Published: (2025)

Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining
by: Peng, Yongqian, et al.
Published: (2025)

A chemical language model for reticular materials design
by: Menon, Dhruv, et al.
Published: (2026)

General time-reversal equivariant neural network potential for magnetic materials
by: Yu, Hongyu, et al.
Published: (2022)

A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
by: Grega, Ivan, et al.
Published: (2025)

Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving
by: Ye, Kangfeng, et al.
Published: (2023)

Leveraging large language models for structured information extraction from pathology reports
by: Balasubramanian, Jeya Balaji, et al.
Published: (2025)

Gel-Chemistry-Dependent Heavy-Metal Ion Transport and Immobilization in Cementitious Nanopores: A Molecular Dynamics Study
by: Chen, Weiqiang, et al.
Published: (2026)

Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
by: Wang, Hongchen, et al.
Published: (2024)

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery
by: Rothfarb, Samuel, et al.
Published: (2025)

Benchmarking large language models for materials synthesis: the case of atomic layer deposition
by: Yanguas-Gil, Angel, et al.
Published: (2024)

MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models
by: Yoshitake, Michiko, et al.
Published: (2026)

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
by: Gouvêa, Rogério Almeida, et al.
Published: (2026)

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
by: Cooper, A. Feder, et al.
Published: (2026)

Beyond designer's knowledge: Generating materials design hypotheses via large language models
by: Liu, Quanliang, et al.
Published: (2024)

Mathematics with large language models as provers and verifiers
by: Duc, Hieu Le, et al.
Published: (2025)

Large language models as oracles for instantiating ontologies with domain-specific knowledge
by: Ciatto, Giovanni, et al.
Published: (2024)

Reshaping MOFs text mining with a dynamic multi-agents framework of large language model
by: Lin, Zuhong, et al.
Published: (2025)

A comparative study of transformer models and recurrent neural networks for path-dependent composite materials
by: Uvdal, Petter, et al.
Published: (2026)

BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning
by: Hage, Tarjei Paule, et al.
Published: (2026)

Higher-Order Knowledge Representations for Agentic Scientific Reasoning
by: Stewart, Isabella A., et al.
Published: (2026)

Are LLMs Ready for Real-World Materials Discovery?
by: Miret, Santiago, et al.
Published: (2024)

Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
by: Buehler, Markus J.
Published: (2025)

Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
by: Vuong, An, et al.
Published: (2025)

Automated Extraction of Material Properties using LLM-based AI Agents
by: Ghosh, Subham, et al.
Published: (2025)

Discovery of sustainable energy materials via the machine-learned material space
by: Grunert, Malte, et al.
Published: (2025)

ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature
by: Roy, Aritra, et al.
Published: (2025)

Materials science in the era of large language models: a perspective
by: Lei, Ge, et al.
Published: (2024)

A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials
by: Cheng, Xinlun, et al.
Published: (2025)