Note: This talk is available over Zoom.

Title: Transforming Materials and Molecular Discovery with Data-Driven Approaches

Abstract: Tackling global challenges like water scarcity and sustainable synthesis demands innovative approaches that integrate functional materials design with AI-driven tools. In this seminar, I will first describe how I engineered metal-organic frameworks (MOFs)—a class of porous crystalline solids—for atmospheric water harvesting, a critical step toward addressing the water–energy nexus. By combining gas sorption measurements with structural characterization techniques (e.g., X-ray diffraction and spectroscopic analyses), I established key design rules for hygroscopic MOFs, optimizing pore size, working capacity, energy efficiency, and scalability. These findings led to the development of portable water-capture devices, successfully field-tested in the extreme conditions of Death Valley National Park, underscoring their real-world potential.

In the second part, I will introduce the integration of large language models (LLMs) into closed-loop porous materials discovery and electrochemical synthesis planning, respectively. As a prime example of human-AI collaboration, my work has enabled efficient literature data mining, accelerated inverse design, and automated synthesis and characterization. By streamlining the exploration of synthesis-structure-property relationships, such LLM-assisted workflows not only expedite material development but also hold great promise for advancing self-driving labs, paving the way for scalable, autonomous, and sustainable solutions in chemical engineering.

Bio: Dr. Zhiling (Zach) Zheng obtained his B.A. in Chemistry from Cornell University, having worked as an undergraduate researcher in the laboratory of Professor Kyle Lancaster. During his Ph.D. under Professor Omar Yaghi as a Kavli Graduate Student Fellow at UC Berkeley, Dr. Zheng developed water harvesting MOFs, while opening a new window into LLM-driven materials research. In his postdoctoral work at MIT ChemE, Dr. Zheng worked with Professor Klavs Jensen to integrate machine learning with automation platforms, aiming to accelerate reaction discovery in electrochemistry. More recently, he became a BIDMaP Fellow at UC Berkeley EECS where he explores deep learning to advance materials discovery.