Oufan Zhang

Center for Theoretical Biological Physics

Papers

Total Citations

145

H-Index

About

Oufan Zhang is a pioneering researcher at the intersection of artificial intelligence and materials chemistry, whose work has redefined how we accelerate the discovery and optimization of advanced porous materials. His primary research areas include AI-driven laboratory automation, machine learning for materials synthesis, and the rational design of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs). Zhang’s most notable contribution is the development of a groundbreaking multi-AI-driven system that integrates ChatGPT, Bayesian optimization, and seven large language model-based assistants to autonomously orchestrate complex chemical experiments. This work, published in 2023 and already garnering 145 citations, demonstrated the first seamless collaboration between multiple AI agents to optimize the crystallinity of MOFs and COFs, dramatically reducing trial-and-error in synthesis. By bridging natural language processing with experimental robotics, Zhang has opened new pathways for autonomous laboratories, enabling researchers to design, execute, and refine experiments with unprecedented speed. His innovative approach not only showcases the transformative potential of large language models in chemistry but also sets a new standard for human-AI collaboration in scientific discovery.