Generative AI for Method Development in Analytical Chemistry: A New Paradigm in Experimental Design and Optimization

The fast development of artificial intelligence (AI), especially generative AI models, is changing the environment of analytical chemistry. As classical method generation in analytical methods relies on manual trial-and-error methodology as well as statistical methods, generative AI is a new paradigm with automated generation of experimental methodology and optimization. In this paper, the authors discuss the use of generative AI-based technologies, including large language models (LLMs) and neural network-based generators, to create new, efficient, and customized methods of analysis. The paper examines existing applications, technology frameworks, and issues and offers a roadmap with regards to the future incorporation of generative AI into daily analytical processes. Finally, the review indicates the promise of generative AI to reinvent analytical chemistry as a highly empirical research area into a predictive, data-driven research area.