mlsanityData to 14 Oct 2026

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-inspired Materials

arXiv 2309.08788 ↗First posted 15 Sep 2023cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.softcited by 30

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Abstract (arXiv)

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model was finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further enhanced with enhanced reasoning ability, as well as with retrieval-augmented generation to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has been shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model showed impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 1 of its 69 references that are in the map. First posted on arXiv 15 Sep 2023.

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1 of 69 references.

References · 1

  1. 2023MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problemscited by 29

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 16; 2025: 8; 2026: 6.

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  1. 2024Evolutionary Computation in the Era of Large Language Model: Survey and Roadmapcited by 90
  2. 2024Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiescited by 69
  3. 2024X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular Designcited by 42
  4. 2024AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligencecited by 36
  5. 2025Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoningcited by 33
  6. 2024Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Surveycited by 17
  7. 2024PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinkingcited by 17
  8. 2024Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understandingcited by 15
  9. 2024Generative Hierarchical Materials Searchcited by 13
  10. 2024Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Designcited by 9
  11. 2026GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Designcited by 9
  12. 2024LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automatacited by 8
18 more
  1. 2025BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experimentscited by 7
  2. 2024Retrieval Augmented Thought Process for Private Data Handling in Healthcarecited by 5
  3. 2024Learning the rules of peptide self-assembly through data mining with large language modelscited by 5
  4. 2025Multimodal machine learning with large language embedding model for polymer property predictioncited by 5
  5. 2024PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation Modelcited by 4
  6. 2026Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombinationcited by 4
  7. 2024A Misleading Gallery of Fluid Motion by Generative Artificial Intelligencecited by 3
  8. 2024Evaluating the Efficacy of Large Language Models in Identifying Phishing Attemptscited by 2
  9. 2024Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devicescited by 1
  10. 2026Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Modelcited by 1
  11. 2025KoGNER: A Novel Framework for Knowledge Graph Distillation on Biomedical Named Entity Recognitioncited by 0
  12. 2025Bridging AI and Carbon Capture: A Dataset for LLMs in Ionic Liquids and CBE Researchcited by 0
  13. 2025BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Miningcited by 0
  14. 2025Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materialscited by 0
  15. 2025Large language models management of medications: three performance analysescited by 0
  16. 2026Bioinspired123D: Generative 3D Modeling System for Bioinspired Structurescited by 0
  17. 2026Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidencecited by 0
  18. 2026Learning from fracture: Teaching language models to design through physical experiencecited by 0

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  1. 2025Large language models in materials science and the need for open-source approaches
  2. 2023Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
  3. 2023MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities
  4. 2025Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents
  5. 2024Are LLMs Ready for Real-World Materials Discovery?
  6. 2024A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language
  7. 2026MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models
  8. 2025Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review