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Large Language Models-assisted Literature Analysis Towards Photocatalysis: a Case of Artificial Nitrogen Photofixation
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DOI：10.1007/s40242-026-6125-x.png)
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Abstract
En 中文
Artificial nitrogen photofixation enables green ammonia synthesis under ambient conditions, making it one of the cutting-edge technologies in the fields of energy transition and sustainable development. Rapid growth in nitrogen photofixation has yielded a massive volume of publications, posing new challenges for manual literature screening, mechanism integration, and future trend analyses. Large language models (LLMs), with their robust capabilities in semantic understanding, information extraction, and logical reasoning, can significantly facilitate literature mining in photocatalysis. Taking artificial nitrogen photofixation as a case study, this perspective constructs an LLM-assisted literature analysis system and explores the practical value of intelligent analytical technologies in view of the emerging tendency. Furthermore, we also explore the anticipated contributions and challenges of artificial intelligence in photocatalysis, particularly regarding material design, experimental optimization, and mechanism investigation, with the aim of establishing a forward-looking roadmap for a low-carbon future in photocatalysis.
Keywords:
Large language model
Nitrogen fixation
Photocatalysis
Ammonia
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