Zhiling Zheng

Papers

Total Citations

245

H-Index

About

Zhiling Zheng is a pioneering researcher at the intersection of artificial intelligence and reticular chemistry, whose work is redefining how materials are discovered and optimized. His primary research areas include the application of large language models (LLMs) and machine learning to the synthesis of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs). Zheng’s most notable contribution is the development of a multi-AI-driven system, detailed in his highly cited 2023 paper (145 citations), which integrates ChatGPT and Bayesian optimization to autonomously orchestrate laboratory experiments—a breakthrough that dramatically accelerates the optimization of crystallinity in porous materials. In his 2025 work (100 citations), he further advanced the field by demonstrating how LLMs can be tailored for reticular chemistry, enabling intelligent prediction and design of novel frameworks. With over 245 citations across his top papers, Zheng’s impact is evident in his innovative fusion of AI agents with experimental chemistry, offering a blueprint for autonomous materials discovery. His achievements position him as a leading voice in the emerging field of AI-driven reticular chemistry, inspiring a new generation of researchers to harness machine learning for complex chemical synthesis.