Enhancing catalysis studies with chat generative pre-trained transformer (ChatGPT): Conversation with ChatGPT

Dalton transactions (Cambridge, England : 2003)(2024)

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摘要
The progress made in natural language processing (NLP) and large language models (LLMs), such as generative pre-trained transformers, (GPT) has provided exciting opportunities for enhancing research across various fields. Within the realm of catalysis studies, GPT-driven models present valuable support in expediting the exploration and comprehension of catalytic processes. This research underscores the significance of ChatGPT in catalysis research, emphasizing its prowess as a valuable tool for furthering scientific inquiries. It suggests that for an outstanding oxygen evolution reaction (OER) catalyst as a case study, scientists can leverage ChatGPT to extract deeper insights and brainstorm innovative approaches to grasp the mechanism better and refine current systems. This study describes the integration of generative pre-trained transformer and similar large language models in catalysis research, highlighting their potential to revolutionize understanding and innovation in oxygen-evolution reaction catalysts.
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