Research
ChatGPT for Chemistry
Currently, one of my main research topics is leveraging the latest advances in Large Language Models (LLMs) such as ChatGPT and related models GPT-3.5, GPT-4, and GPT-4V to explore various applications. These applications include literature synthesis, conditioned text mining, image mining, research planning, project guidance and consultation, chemical structure editing, AI-assisted coding, safety regulation, and robotic platform operation. These models are easily “programmable” through natural human language, thereby reducing the need for entry-level researchers to have coding skills and making them easy to use. The ultimate goal is to harness the collective “intelligence” from various LLM-based assistants in many areas to help humans simplify task modularity and even automate the entire workflow in laboratories, thereby accelerating the discovery of materials and drugs.
Related Papers:
Z. Zheng; O. Zhang; C. Borgs; J. T. Chayes; O. M. Yaghi, ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis. JACS (2023)
Z. Zheng; Z. Rong; N. Rampal; C. Borgs; J. T. Chayes; O. M. Yaghi, A GPT-4 Reticular Chemist for MOF Discovery. Angew. Chem. Int. Ed. (2023)
Z. Zheng; A. H. Alawadhi; S. Chheda; S. E. Neumann; N. Rampal; S. Liu; H. L. Nguyen; Y.-H. Lin; Z. Rong; J. I. Siepmann; L. Gagliardi; A. Anandkumar; C. Borgs; J. T. Chayes; O. M. Yaghi, Shaping the Water Harvesting Behavior of Metal-Organic Frameworks Aided by Fine-Tuned GPT Models. JACS (2023)
Z. Zheng; Z. He; S. Chheda; O. Khattab; N. Rampal; M. A. Zaharia; C. Borgs; J. T. Chayes; O. M. Yaghi, Image and Data Mining in Reticular Chemistry Powered by GPT-4V. Digital Discovery (2024)
Automated Synthesis Exploration Systems
One of my current research efforts aim to develop the automated platform for the exploration and optimization of synthesis reactions. The central objective is to establish an AI-driven laboratory environment where minimal human intervention is required. This system will integrate two core components: a programmable AI “brain” and a precise mechanical “arm.” The AI “brain” will be responsible for strategizing and planning the next steps in the synthesis process, while the mechanical “arm” will execute these steps autonomously. This synergy aims to create a closed-loop system that continuously optimizes synthesis reactions, thereby enhancing efficiency, accuracy, and reproducibility in chemical research.
Related Papers:
Z. Zheng; O. Zhang; H. L. Nguyen; N. Rampal; A. H. Alawadhi; Z. Rong; T. Head-Gordon; C. Borgs; J. T. Chayes; O. M. Yaghi, ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs. ACS Cent. Sci. (2023)
S. Liu; W. Du; Y. Li; Z. Li; Z. Zheng; C. Duan; Z. Ma; O. M. Yaghi; A. Anandkumar; C. Borgs; J. T. Chayes; H. Guo; J. Tang, Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials. NeurIPS (2023)
X. Han; Z. Zhou; K. Wang; Z. Zheng; S. E. Neumann; T. Ma; O. M. Yaghi, Crystalline Polyphenylene Covalent Organic Frameworks. JACS (2024)
Atmospheric Water Harvesting
Atmospheric water harvesting, the process of extracting water directly from the air, is emerging as a promising solution to the global challenge of water scarcity. This technique is advantageous as it is not limited by geographic constraints and can be operational throughout the year. My contributions to this field center at the development of metal-organic frameworks (MOFs) for water harvesting. This includes designing effective sorbents, optimizing environmentally-friendly synthesis methods, scaling up the production of these materials to kilogram levels, and constructing a water harvesting device. Furthermore, I have successfully tested this device in extreme conditions, such as those found in Death Valley. This research not only addresses a critical environmental issue but also paves the way for innovative methods in sustainable water resource management.
Related Papers:
Z. Zheng; N. Hanikel; H. Lyu; O. M. Yaghi, Broadly Tunable Atmospheric Water Harvesting in Multivariate Metal–Organic Frameworks. JACS (2022)
Z. Zheng; H. L. Nguyen; N. Hanikel; K.-K. Li; Z. Zhou; T. Ma; O. M. Yaghi, High-Yield, Green and Scalable Methods for Producing MOF-303 for Water Harvesting from Desert Air. Nat. Protoc. (2023)
W. Song; Z. Zheng; A. H. Alawadhi; O. M. Yaghi, MOF Water Harvester Produces Water from Death Valley Desert Air in Ambient Sunlight. Nat. Water (2023)
S. Neumann; K. Neumann; Z. Zheng; N. Hanikel; J. Tsao; O. M. Yaghi, Harvesting Water in the Classroom. J. Chem. Educ. (2023)
A. H. Alawadhi; S. Chheda; G. Stroscio; Z. Rong; D. Kurandina; H. L. Nguyen; N. Rampal; Z. Zheng; L. Gagliardi; O. M. Yaghi, Harvesting Water from Air with High-Capacity, Stable Furan-Based Metal-Organic Frameworks. JACS (2024)
Design and Synthesis of MOFs
My research focuses on the intricate field of reticular chemistry, which involves crafting crystalline extended structures by linking molecular building units through strong bonds. With a background in experimental chemistry acquired during my PhD, I am deeply passionate about conducting laboratory experiments. My work primarily involves the design and synthesis of metal-organic frameworks (MOFs), zeolitic imidazolate frameworks (ZIFs), and covalent organic frameworks (COFs). I aim to explore diverse topologies and discover new compounds using various methods and strategies. Furthermore, I am committed to developing techniques to produce MOFs in both high yield and large quantities, ranging from grams to kilograms. An emerging area of interest for me is integrating artificial intelligence to enhance the efficiency and precision of MOF synthesis, aligning with the growing trend of incorporating technology in chemical research.
Related Papers:
Z. Zheng; Z. Rong; H. L. Nguyen; O. M. Yaghi, Structural Chemistry of Zeolitic Imidazolate Frameworks. Inorg. Chem. (2023)
Z. Zheng; Z. Rong; O. I.-F. Chen; O. M. Yaghi, Metal-Organic Frameworks with Rod Yttrium Secondary Building Units. Isr. J. Chem. (2023)
Z. Zheng; A. H. Alawadhi; O. M. Yaghi, Green Synthesis and Scale-Up of MOFs for Water Harvesting from Air. Mol. Front. J. (2023)
N. Hanikel; D. Kurandina; S. Chheda, Z. Zheng; Z. Rong; S. E. Neumann; J. Sauer; J. I. Siepmann; L. Gagliardi, O. M. Yaghi, MOF Linker Extension Strategy for Enhanced Atmospheric Water Harvesting. ACS Cent. Sci. (2023)