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Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Paper exploring the use of large language models in materials analysis, design, and manufacturing.

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Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

By Markus J. BuehlerACS Engineering Au
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The paper presents a fine-tuned model, MechGPT, developed for mechanics of materials domain. It explores retrieval-augmented Ontological Knowledge Graph strategies to address limitations of LLMs when queried outside learned context.

The approach improves generative performance and provides mechanistic insights for material design process.

Abstract

The paper presents a fine-tuned model, MechGPT, developed for mechanics of materials domain. It explores retrieval-augmented Ontological Knowledge Graph strategies to address limitations of LLMs when queried outside learned context. The approach improves generative performance and provides mechanistic insights for material design process.

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generative retrieval-augmented ontologic graphmultiagent strategiesinterpretive large language model-based materials designmechgptknowledge graphlarge language modelsLarge Language ModelsRetrieval & RAGOntology & TaxonomySemantic Interoperability
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