Herbert Wertheim College of Engineering
Hennig Materials Theory Lab
Led by Richard Hennig · Materials Science and Engineering
What we study
The lab uses computer models and machine learning to study materials like superconductors and nanomaterials. They develop tools to predict and design new materials using AI and quantum simulations.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Papers
- 5
Recent papers, explained
- MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching
2026 · Journal of Chemical Information and Modeling
Integrates advanced guidance strategies into a molecular generation framework to improve chemical validity and diversity.
- Kinetics of monolayer and bilayer graphene nano-ribbon formation on 3C-SiC: Influence of substrate geometry and growth conditions
2026 · Acta Materialia
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- Towards the discovery of high critical magnetic field superconductors
2026 · npj Computational Materials
Builds a database of critical magnetic fields for superconductors to guide material discovery.
- How Accurately Can We Describe Spin Crossover?
2026 · Journal of Chemical Theory and Computation
Creates a method to predict spin crossover temperatures in metal complexes using computational models.
- Benchmarking of fast and interpretable UF machine learning potentials
2026 · Computational Materials Science
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Richard Hennig hasn't set up this page yet. Their UF Scholars profile lists their publications.