Zhiling Zheng

Zhiling  Zheng

Zhiling Zheng

Assistant Professor of Chemistry
PHD, UNIVERSITY OF CALIFORNIA, BERKELEY (2023)
BA, CORNELL UNIVERSITY (2019)
research interests:
  • Materials Chemistry
  • Reticular Chemistry
  • Metal-Organic Frameworks
  • Large Language Models
  • Chemical Separations
  • Crystalline Porous Materials
  • High-Throughput Experimentation
  • Data Science and Machine Learning
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The Zheng group leverages data-driven chemistry to simplify materials design and synthesis.

 

We are a multidisciplinary research team integrating AI with synthetic chemistry to accelerate materials discovery. By employing organic, inorganic, and solid-state synthesis strategies, we create novel, programmable crystalline structures with targeted properties, aiming to lead a future where human–AI collaborative workflows are routinely accessible to all laboratories. Our research focuses on several main directions:
 

Digital Reticular Chemistry

We are exploring the integration of large language models (LLMs) and autonomous AI agents to transform chemical research. Chemistry itself is a language, and our AI agents communicate directly with chemists using natural language through low-code/no-code interfaces, enabling automated literature data mining and robotic experimentation and making workflows user-friendly for synthetic chemists.
 

Preditive Synthesis of Framework Materials
 


Traditional synthesis of crystalline materials relies on trial-and-error across multidimensional parameters (e.g., temperature, concentration, ratios, etc.). Our lab integrates chemical knowledge with machine learning to answer the long standing “crystallization challnges” in reticular chemistry: can we predict under which conditions metals and organic linkers crystallize into desired frameworks?

 

Rational Design of Novel Nano-MOFs

The modularity of reticular chemistry—connecting molecular building blocks into frameworks with predetermined structures and functionalities—offers vast opportunities. We are particularly excited about advancing new design principles and more accessible synthetic methodologies for the (i) high-entropy MOFs and (ii) biocompatible MOFs that can be prepared at the nanoscale and applied in energy, environmental, and therapeutic applications.

Selected Publications

Large Language Models for Reticular Chemistry. Nature Reviews Materials, 10, 369–381 (2025). DOI: 10.1038/s41578-025-00772-8

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions. Angewandte Chemie, 64, e202418074 (2025). DOI: 10.1002/anie.202418074

ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis. Journal of the American Chemical Society, 145, 18048–18062 (2023). DOI: 10.1021/jacs.3c05819

ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs. ACS Central Science, 9, 2161–2170 (2023). DOI: 10.1021/acscentsci.3c01087

High-Yield, Green, and Scalable Methods for Producing MOF-303 for Water Harvesting from Desert Air. Nature Protocols, 18, 136–156 (2023). DOI: 10.1038/s41596-022-00756-w

Broadly Tunable Atmospheric Water Harvesting in Multivariate Metal–Organic Frameworks. Journal of the American Chemical Society, 144, 22669–22675 (2022). DOI: 10.1021/jacs.2c09756

Selected Awards

Inflection Award, 2025

Carbon Future Young Investigator Award, 2025

Dream Chemistry Award Finalist, 2024

BIDMaP Scholar, 2024

Kavli ENSI Graduate Student Fellowship, 2023

Merrill Presidential Scholar, 2019