Jennifer Chayes

University of California, Berkeley, Blue Planet (Norway)

Papers

Total Citations

245

H-Index

About

Jennifer Chayes is a pioneering figure at the intersection of artificial intelligence and reticular chemistry, where she has redefined how machine learning accelerates materials discovery. Her research focuses on developing multi-agent AI systems and large language models (LLMs) to optimize the synthesis of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs). In her landmark 2023 work, cited over 145 times, Chayes introduced a ChatGPT-driven research group that integrates seven LLM-based assistants with Bayesian optimization and machine learning algorithms to autonomously orchestrate laboratory workflows—from experimental design to crystallinity optimization. She further advanced the field with a 2025 paper (100+ citations) that systematically applies LLMs to reticular chemistry, enabling rapid prediction and rational design of porous materials. Chayes’ contributions are notable for bridging generative AI with experimental chemistry, dramatically reducing the time and cost of discovering next-generation materials for gas storage, catalysis, and carbon capture. Her work has been recognized as a transformative step toward fully autonomous laboratories, positioning her as a leader in AI-accelerated scientific discovery.