Liberal Arts and Sciences
Liu Lab
Led by Mingjie Liu · Chemistry
What we study
The Liu Lab uses AI methods—generative models, deep learning, and optimization—to speed up chemical and materials discovery. They combine computer simulations with experiments to create new solutions for energy, environmental, and health challenges.
What working here is like
- Computational
- Wet lab
- No organisms (data only)
- Coding required
- Projects
- 2
- Papers
- 5
Current projects
Synergetic Effects of Transition Metal Atoms Anchored on Graphdiyne: A Comparative Analysis of Finite and Periodic Models
The lab compares how transition metal atoms behave on graphdiyne using computer simulations. Students run density functional theory calculations on both finite and periodic models, looking at geometry, charge, and formation energy, and compare the results to see which model best matches the real material.
From the Spring 2025 UF Undergraduate Research Symposium · abstract book, p. 248
Atomic Precision Nanoparticle Synthesis Forecaster
The lab works on tiny semiconductor particles that could be used in solar cells and LEDs. The student applied machine‑learning models to predict how recipe changes affect the particles’ optical properties and helped refine the models with new experiments.
From the Spring 2025 UF Undergraduate Research Symposium · abstract book, p. 89
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- MAGICV: A Graphical Web Interface for Rapid Simulation of Cyclic Voltammograms for Complex Mechanisms
2026 · ACS electrochemistry.
Builds a web tool to easily simulate complex chemical reactions for students.
- MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching
2026 · Journal of Chemical Information and Modeling
Show 3 more papersShow fewer
- Transferable Neural Network Potential for Elastic and Vibrational Properties of Carbon, Hydrogen, Oxygen, and Nitrogen-Based Two Dimensional Covalent Organic Frameworks
2026 · Chemistry of Materials
Creates a computer model to predict properties of 2D materials for energy storage.
- COFFlow: Building-Block-Based Generative Modeling for Two-Dimensional Covalent Organic Framework Discovery
2026 · Chemistry of Materials
Develops a tool to predict and design new materials called 2D covalent organic frameworks.
- Transparent reporting for agentic catalysis enabled by artificial intelligence: Community guidelines and a publication checklist
2026 · Chem Catalysis
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Mingjie Liu hasn't set up this page yet. Their UF Scholars profile lists their publications.