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AI Framework Speeds Up Relay Catalysis Design With Transparent, Literature-Linked Pathways

Researchers from Xiamen University and the University of Edinburgh developed an AI-assisted framework that combines large language models with a catalysis knowledge graph (Cat-KG) to design relay catalysis pathways. The system rapidly finds, explains, and verifies multi-step reaction routes with links to original literature.

26 Aug 2025

Generative AI Transforms Scientific Discovery with Knowledge Graphs

Researcher Markus J. Buehler integrated generative AI and graph theory to uncover interdisciplinary connections, revealing novel insights in bioinspired materials and art-inspired design.

21 Nov 2024

Enhanced Language Models for E-Learning

Researchers have developed an advanced method to augment large language models (LLMs) with domain-specific knowledge for E-learning, significantly improving their performance in generating accurate and contextually relevant content.

26 Jun 2024

LLMs Boosts Scientific Research Discovery

Researchers explored using large language models (LLMs) like GPT-3.5, Llama 2, and Mistral to suggest research properties beyond traditional keywords, aiming to enhance scientific findability. Comparing LLM-generated properties with manually curated ones from the open research knowledge graph (ORKG), they found LLMs promising but recommended further refinement for better alignment with scientific tasks and human expertise

13 Jun 2024

Geological Knowledge Graph Analysis with Graph Attention Networks

This paper introduces a novel method for mining knowledge graph patterns in geological structures using graph attention networks (GAT). By treating subgraph structures as queries and employing an adaptive edge deletion mechanism, the method achieves optimal matching subgraphs.

10 May 2024